Audio zoom based on speaker detection using lip reading

ABSTRACT

Disclosed are an electronic device performing an audio zoom based on speaker detection using lip reading and a method for controlling the electronic device. According to an embodiment, the electronic device detects a direction of a sound source while recording a video and determines a speaker&#39;s direction via facial recognition and mouth shape recognition in the sound source direction. Microphone beamforming may be performed based on the speaker&#39;s direction. Thus, the accuracy of audio zoom may be enhanced.

CROSS-REFERENCE TO RELATED APPLICATION(S)

Pursuant to 35 U.S.C. § 119(a), this application claims the benefit of earlier filing date and right of priority to Korean Patent Application No. 10-2020-0045736, filed on Apr. 16, 2020, the contents of which are hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

The disclosure relates to an electronic device performing an audio zoom based on speaker detection using lip reading and a method for controlling the electronic device.

DESCRIPTION OF RELATED ART

An artificial intelligence (AI) system is a computer system which implements human level intelligence, and this system is self-trained and determines and thus becomes smarter unlike conventional rule-based smart systems. The more used, the more precisely AI systems may perceive and understand users' preference. Thus, legacy rule-based smart systems are being gradually replaced with deep learning-based AI systems.

Artificial intelligence technology consists of machine learning (deep learning) and element techniques using machine learning. Machine learning is an algorithm technique that it itself may classify and learn the features of input data. The component technology is a technique for mimicking the human brain's perception and decision capabilities using a machine learning algorithm (e.g., deep learning), and this may be divided into several technical fields, such as linguistic understanding, visual understanding, inference/prediction, knowledge expression, and operation control.

Recently, artificial intelligence technology has more various applications in speech recognition.

SUMMARY

Upon obtaining a speaker's speech while recording a video, despite use of an audio zoom, sound collection performance may be lowered due to an inability of accurately setting the speaker's direction.

The disclosure aims to address the foregoing issues and/or needs.

Further, the disclosure provides an electronic device capable of enhancing the accuracy of audio zoom while recording a video by estimating the speaker's position via speech analysis and a method for controlling the electronic device.

Further, the disclosure provides an electronic device capable of enhancing the accuracy of audio zoom by more precisely setting the sound collection direction of a microphone by specifying the speaker positioned in the estimated direction by a more accurate method and a method of controlling the electronic device.

According to an embodiment of the disclosure, an electronic device comprises a camera capturing a video, at least one microphone obtaining a sound while capturing the video, and a processor detecting a direction of a sound source of the sound based on a sound feature of the obtained sound, recognizing a face and mouth shape of a person positioned in the direction of the sound, and recognizing a speaker issuing an utterance by lip reading analysis, wherein the processor performs an audio zoom by performing beamforming of the microphone based on a direction of the recognized speaker.

The processor may recognize the face of the person positioned in the direction of the sound based on a first learning model trained for facial recognition from an image.

The first learning model may include an artificial neural network, and wherein results output from the artificial neural network include information for the speaker's face and positions of the speaker's eyes, nose, and mouth in the face.

The first learning model may be a deep learning model for facial recognition. After facial recognition is performed in a cloud server or edge computing environment, the result of facial recognition may be transferred to the electronic device.

Or, facial recognition may be performed by applying a deep learning model to an image captured by the electronic device.

The processor may recognize that the speaker is issuing the utterance based on a variation in the recognized mouth shape of the speaker based on a second learning model.

The processor amplify a voice generated from a direction of the speaker and reduce sounds from other directions than the direction of the speaker.

Upon recognizing a variation in the speaker's position, the processor may control the beamforming of the microphone based on the varied position.

The processor may recognize the variation in the speaker's position based on an image obtained via the camera.

Upon determining that the sound obtained via the microphone includes a human voice based on analysis of the sound obtained via the microphone, the processor may detect the sound source direction.

When the at least one microphone includes a plurality of microphones, the processor may separate sounds obtained via the plurality of microphones into as many sound sources as the number of the plurality of microphones and recognize the person's face and mouth shape based on a direction of each sound source.

When the number of the sound sources is larger than the number of the plurality of microphones, the processor may group at least two or more of a plurality of speakers based on a distance between the plurality of speakers and control to perform the audio zoom, with the speaker group covered by a beamforming direction of one of the plurality of microphones.

When the number of the sound sources is smaller than the number of the plurality of microphones, the processor may control to allow at least two or more of the plurality of microphones to have the same beamforming direction according to a predetermined reference.

The at least one microphone may include at least one of an omni-directional microphone that reacts, at the same sensitivity, to sounds from all directions, a uni-directional microphone that reacts only to sounds from the front in a 180 degree range at a sensitivity not less than a threshold, or a super-directional microphone that reacts only to sounds from the front in a 30 degree range at a sensitivity not less than the threshold.

Upon recognizing the speaker's utterance after the speaker's direction is recognized and a direction of the beamforming is determined, the processor may adjust the gain of the microphone in the beamforming direction.

The processor may adjust the gain of the microphone depending on a distance from the speaker being captured via the camera.

The processor may store, in a memory, the video together with text corresponding to a result of speech recognition on the speaker's utterance according to the audio zoom.

The processor may map the text corresponding to the result of speech recognition to the speaker's mouth shape and display the text-mapped mouth shape on a display while playing the stored video.

Upon recognizing the speaker's direction, the processor may display guide information for setting a direction of the beamforming on a display.

The guide information may include graphic objects corresponding to the number of the at least one microphone. The processor may control to set a beamforming direction of a specific microphone to face a specific speaker according to reception of a gesture input to associate a specific graphic object with the specific speaker.

The captured video may be stored in a storage unit, with voice data and image data of the video synchronized. When a predetermined number of frames or more are stored in the storage unit, the processor may analyze each of the image data and the voice data.

According to an embodiment of the disclosure, a method of controlling an electronic device comprises capturing a video via a camera, obtaining a sound via at least one microphone while capturing the video, detecting a direction of a sound source of the sound based on a sound feature of the obtained sound, recognizing a face and mouth shape of a person positioned in the sound source direction of the sound and recognizing a speaker issuing an utterance by lip reading analysis, and performing an audio zoom by controlling a beamforming direction of the microphone to face the recognized speaker.

The disclosure may enhance the accuracy of audio zoom while recording a video by estimating the speaker's position via speech analysis and a method for controlling the electronic device.

Further, the disclosure may enhance the accuracy of audio zoom by more precisely setting the sound collection direction of a microphone by specifying the speaker positioned in the estimated direction by a more accurate method and a method of controlling the electronic device.

BRIEF DESCRIPTION OF THE DRAWINGS

Accompanying drawings included as a part of the detailed description for helping understand the disclosure provide embodiments of the disclosure and are provided to describe technical features of the disclosure with the detailed description.

FIG. 1 illustrates one embodiment of an AI System.

FIG. 2 is a block diagram of a wireless communication system to which methods proposed in the disclosure are applicable.

FIG. 3 shows an example of a signal transmission/reception method in a wireless communication system.

FIG. 4A is a block diagram of a mobile terminal in accordance with the disclosure.

FIGS. 4B and 4C are conceptual views of one example of the mobile terminal, viewed from different directions.

FIG. 5 shows an example of basic operations of AI processing in a 5G communication system.

FIG. 6 is a block diagram of an AI device according to an embodiment of the disclosure.

FIG. 7 is a flowchart illustrating a method for controlling an electronic device according to an embodiment of the disclosure.

FIG. 8 is a view illustrating S730 of FIG. 7.

FIG. 9 is a view illustrating an example of detecting a speaker's utterance based on a variation in the mouth shape based on facial recognition.

FIGS. 10A, 10B, and 10C are views illustrating the orientation characteristics of a microphone according to an embodiment of the disclosure.

FIG. 11 is a flowchart illustrating a method for controlling an electronic device according to another embodiment of the disclosure.

FIG. 12 is a flowchart illustrating a method for controlling an electronic device according to an embodiment of the disclosure.

FIG. 13 is a view illustrating the embodiment of FIG. 12.

FIG. 14 is a view illustrating an example of setting a microphone's beamforming direction according to an embodiment of the disclosure.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the disclosure will be described in detail with reference to the attached drawings. The same or similar components are given the same reference numbers and redundant description thereof is omitted. The suffixes “module” and “unit” of elements herein are used for convenience of description and thus can be used interchangeably and do not have any distinguishable meanings or functions. Further, in the following description, if a detailed description of known techniques associated with the disclosure would unnecessarily obscure the gist of the disclosure, detailed description thereof will be omitted. In addition, the attached drawings are provided for easy understanding of embodiments of the disclosure and do not limit technical spirits of the disclosure, and the embodiments should be construed as including all modifications, equivalents, and alternatives falling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describe various components, such components must not be limited by the above terms. The above terms are used only to distinguish one component from another.

When an element is “coupled” or “connected” to another element, it should be understood that a third element may be present between the two elements although the element may be directly coupled or connected to the other element. When an element is “directly coupled” or “directly connected” to another element, it should be understood that no element is present between the two elements.

The singular forms are intended to include the plural forms as well unless the context clearly indicates otherwise.

In addition, in the specification, it will be further understood that the terms “comprise” and “include” specify the presence of stated features, integers, steps, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or combinations.

Hereinafter, 5G communication (5th generation mobile communication) required by an apparatus requiring AI processed information and/or an AI processor will be described through paragraphs A through G.

The three main requirement areas in the 5G system are (1) enhanced Mobile Broadband (eMBB) area, (2) massive Machine Type Communication (mMTC) area, and (3) Ultra-Reliable and Low Latency Communication (URLLC) area.

Some use case may require a plurality of areas for optimization, but other use case may focus only one Key Performance Indicator (KPI). The 5G system supports various use cases in a flexible and reliable manner.

eMBB far surpasses the basic mobile Internet access, supports various interactive works, and covers media and entertainment applications in the cloud computing or augmented reality environment. Data is one of core driving elements of the 5G system, which is so abundant that for the first time, the voice-only service may be disappeared. In the 5G, voice is expected to be handled simply by an application program using a data connection provided by the communication system. Primary causes of increased volume of traffic are increase of content size and increase of the number of applications requiring a high data transfer rate. Streaming service (audio and video), interactive video, and mobile Internet connection will be more heavily used as more and more devices are connected to the Internet. These application programs require always-on connectivity to push real-time information and notifications to the user. Cloud-based storage and applications are growing rapidly in the mobile communication platforms, which may be applied to both of business and entertainment uses. And the cloud-based storage is a special use case that drives growth of uplink data transfer rate. The 5G is also used for cloud-based remote works and requires a much shorter end-to-end latency to ensure excellent user experience when a tactile interface is used. Entertainment, for example, cloud-based game and video streaming, is another core element that strengthens the requirement for mobile broadband capability. Entertainment is essential for smartphones and tablets in any place including a high mobility environment such as a train, car, and plane. Another use case is augmented reality for entertainment and information search. Here, augmented reality requires very low latency and instantaneous data transfer.

Also, one of highly expected 5G use cases is the function that connects embedded sensors seamlessly in every possible area, namely the use case based on mMTC. Up to 2020, the number of potential IoT devices is expected to reach 20.4 billion. Industrial IoT is one of key areas where the 5G performs a primary role to maintain infrastructure for smart city, asset tracking, smart utility, agriculture and security.

URLLC includes new services which may transform industry through ultra-reliable/ultra-low latency links, such as remote control of major infrastructure and self-driving cars. The level of reliability and latency are essential for smart grid control, industry automation, robotics, and drone control and coordination.

Next, a plurality of use cases will be described in more detail.

The 5G may complement Fiber-To-The-Home (FTTH) and cable-based broadband (or DOCSIS) as a means to provide a stream estimated to occupy hundreds of megabits per second up to gigabits per second. This fast speed is required not only for virtual reality and augmented reality but also for transferring video with a resolution more than 4K (6K, 8K or more). VR and AR applications almost always include immersive sports games. Specific application programs may require a special network configuration. For example, in the case of VR game, to minimize latency, game service providers may have to integrate a core server with the edge network service of the network operator.

Automobiles are expected to be a new important driving force for the 5G system together with various use cases of mobile communication for vehicles. For example, entertainment for passengers requires high capacity and high mobile broadband at the same time. This is so because users continue to expect a high-quality connection irrespective of their location and moving speed. Another use case in the automotive field is an augmented reality dashboard. The augmented reality dashboard overlays information, which is a perception result of an object in the dark and contains distance to the object and object motion, on what is seen through the front window. In a future, a wireless module enables communication among vehicles, information exchange between a vehicle and supporting infrastructure, and information exchange among a vehicle and other connected devices (for example, devices carried by a pedestrian). A safety system guides alternative courses of driving so that a driver may drive his or her vehicle more safely and to reduce the risk of accident. The next step will be a remotely driven or self-driven vehicle. This step requires highly reliable and highly fast communication between different self-driving vehicles and between a self-driving vehicle and infrastructure. In the future, it is expected that a self-driving vehicle takes care of all of the driving activities while a human driver focuses on dealing with an abnormal driving situation that the self-driving vehicle is unable to recognize. Technical requirements of a self-driving vehicle demand ultra-low latency and ultra-fast reliability up to the level that traffic safety may not be reached by human drivers.

The smart city and smart home, which are regarded as essential to realize a smart society, will be embedded into a high-density wireless sensor network. Distributed networks comprising intelligent sensors may identify conditions for cost-efficient and energy-efficient conditions for maintaining cities and homes. A similar configuration may be applied for each home. Temperature sensors, window and heating controllers, anti-theft alarm devices, and home appliances will be all connected wirelessly. Many of these sensors typified with a low data transfer rate, low power, and low cost. However, for example, real-time HD video may require specific types of devices for the purpose of surveillance.

As consumption and distribution of energy including heat or gas is being highly distributed, automated control of a distributed sensor network is required. A smart grid collects information and interconnect sensors by using digital information and communication technologies so that the distributed sensor network operates according to the collected information. Since the information may include behaviors of energy suppliers and consumers, the smart grid may help improving distribution of fuels such as electricity in terms of efficiency, reliability, economics, production sustainability, and automation. The smart grid may be regarded as a different type of sensor network with a low latency.

The health-care sector has many application programs that may benefit from mobile communication. A communication system may support telemedicine providing a clinical care from a distance. Telemedicine may help reduce barriers to distance and improve access to medical services that are not readily available in remote rural areas. It may also be used to save lives in critical medical and emergency situations. A wireless sensor network based on mobile communication may provide remote monitoring and sensors for parameters such as the heart rate and blood pressure.

Wireless and mobile communication are becoming increasingly important for industrial applications. Cable wiring requires high installation and maintenance costs. Therefore, replacement of cables with reconfigurable wireless links is an attractive opportunity for many industrial applications. However, to exploit the opportunity, the wireless connection is required to function with a latency similar to that in the cable connection, to be reliable and of large capacity, and to be managed in a simple manner. Low latency and very low error probability are new requirements that lead to the introduction of the 5G system.

Logistics and freight tracking are important use cases of mobile communication, which require tracking of an inventory and packages from any place by using location-based information system. The use of logistics and freight tracking typically requires a low data rate but requires large-scale and reliable location information.

The disclosure to be described below may be implemented by combining or modifying the respective embodiments to satisfy the aforementioned requirements of the 5G system.

FIG. 1 illustrates a conceptual diagram one embodiment of an AI device.

Referring to FIG. 1, in the AI system, at least one or more of an AI server 16, robot 11, self-driving vehicle 12, XR device 13, smartphone 14, or home appliance 15 are connected to a cloud network 10. Here, the robot 11, self-driving vehicle 12, XR device 13, smartphone 14, or home appliance 15 to which the AI technology has been applied may be referred to as an AI device (11 to 15).

The cloud network 10 may comprise part of the cloud computing infrastructure or refer to a network existing in the cloud computing infrastructure. Here, the cloud network 10 may be constructed by using the 3G network, 4G or Long Term Evolution (LTE) network, or 5G network.

In other words, individual devices (11 to 16) constituting the AI system may be connected to each other through the cloud network 10. In particular, each individual device (11 to 16) may communicate with each other through the eNB but may communicate directly to each other without relying on the eNB.

The AI server 16 may include a server performing AI processing and a server performing computations on big data.

The AI server 16 may be connected to at least one or more of the robot 11, self-driving vehicle 12, XR device 13, smartphone 14, or home appliance 15, which are AI devices constituting the AI system, through the cloud network 10 and may help at least part of AI processing conducted in the connected AI devices (11 to 15).

At this time, the AI server 16 may teach the artificial neural network according to a machine learning algorithm on behalf of the AI device (11 to 15), directly store the learning model, or transmit the learning model to the AI device (11 to 15).

At this time, the AI server 16 may receive input data from the AI device (11 to 15), infer a result value from the received input data by using the learning model, generate a response or control command based on the inferred result value, and transmit the generated response or control command to the AI device (11 to 15).

Similarly, the AI device (11 to 15) may infer a result value from the input data by employing the learning model directly and generate a response or control command based on the inferred result value.

<AI+Robot>

By employing the AI technology, the robot 11 may be implemented as a guide robot, transport robot, cleaning robot, wearable robot, entertainment robot, pet robot, or unmanned flying robot.

The robot 11 may include a robot control module for controlling its motion, where the robot control module may correspond to a software module or a chip which implements the software module in the form of a hardware device.

The robot 11 may obtain status information of the robot 11, detect (recognize) the surroundings and objects, generate map data, determine a travel path and navigation plan, determine a response to user interaction, or determine motion by using sensor information obtained from various types of sensors.

Here, the robot 11 may use sensor information obtained from at least one or more sensors among lidar, radar, and camera to determine a travel path and navigation plan.

The robot 11 may perform the operations above by using a learning model built on at least one or more artificial neural networks. For example, the robot 11 may recognize the surroundings and objects by using the learning model and determine its motion by using the recognized surroundings or object information. Here, the learning model may be the one trained by the robot 11 itself or trained by an external device such as the AI server 16.

At this time, the robot 11 may perform the operation by generating a result by employing the learning model directly but also perform the operation by transmitting sensor information to an external device such as the AI server 16 and receiving a result generated accordingly.

The robot 11 may determine a travel path and navigation plan by using at least one or more of object information detected from the map data and sensor information or object information obtained from an external device and navigate according to the determined travel path and navigation plan by controlling its locomotion platform.

Map data may include object identification information about various objects disposed in the space in which the robot 11 navigates. For example, the map data may include object identification information about static objects such as wall and doors and movable objects such as a flowerpot and a desk. And the object identification information may include the name, type, distance, location, and so on.

Also, the robot 11 may perform the operation or navigate the space by controlling its locomotion platform based on the control/interaction of the user. At this time, the robot 11 may obtain intention information of the interaction due to the user's motion or voice command and perform an operation by determining a response based on the obtained intention information.

<AI+Autonomous Navigation>

By employing the AI technology, the self-driving vehicle 12 may be implemented as a mobile robot, unmanned ground vehicle, or unmanned aerial vehicle.

The self-driving vehicle 12 may include an autonomous navigation module for controlling its autonomous navigation function, where the autonomous navigation control module may correspond to a software module or a chip which implements the software module in the form of a hardware device. The autonomous navigation control module may be installed inside the self-driving vehicle 12 as a constituting element thereof or may be installed outside the self-driving vehicle 12 as a separate hardware component.

The self-driving vehicle 12 may obtain status information of the self-driving vehicle 12, detect (recognize) the surroundings and objects, generate map data, determine a travel path and navigation plan, or determine motion by using sensor information obtained from various types of sensors.

Like the robot 11, the self-driving vehicle 12 may use sensor information obtained from at least one or more sensors among lidar, radar, and camera to determine a travel path and navigation plan.

In particular, the self-driving vehicle 12 may recognize an occluded area or an area extending over a predetermined distance or objects located across the area by collecting sensor information from external devices or receive recognized information directly from the external devices.

The self-driving vehicle 12 may perform the operations above by using a learning model built on at least one or more artificial neural networks. For example, the self-driving vehicle 12 may recognize the surroundings and objects by using the learning model and determine its navigation route by using the recognized surroundings or object information. Here, the learning model may be the one trained by the self-driving vehicle 12 itself or trained by an external device such as the AI server 16.

At this time, the self-driving vehicle 12 may perform the operation by generating a result by employing the learning model directly but also perform the operation by transmitting sensor information to an external device such as the AI server 16 and receiving a result generated accordingly.

The self-driving vehicle 12 may determine a travel path and navigation plan by using at least one or more of object information detected from the map data and sensor information or object information obtained from an external device and navigate according to the determined travel path and navigation plan by controlling its driving platform.

Map data may include object identification information about various objects disposed in the space (for example, road) in which the self-driving vehicle 12 navigates. For example, the map data may include object identification information about static objects such as streetlights, rocks and buildings and movable objects such as vehicles and pedestrians. And the object identification information may include the name, type, distance, location, and so on.

Also, the self-driving vehicle 12 may perform the operation or navigate the space by controlling its driving platform based on the control/interaction of the user. At this time, the self-driving vehicle 12 may obtain intention information of the interaction due to the user's motion or voice command and perform an operation by determining a response based on the obtained intention information.

<AI+XR>

By employing the AI technology, the XR device 13 may be implemented as a Head-Mounted Display (HMD), Head-Up Display (HUD) installed at the vehicle, TV, mobile phone, smartphone, computer, wearable device, home appliance, digital signage, vehicle, robot with a fixed platform, or mobile robot.

The XR device 13 may obtain information about the surroundings or physical objects by generating position and attribute data about 3D points by analyzing 3D point cloud or image data acquired from various sensors or external devices and output objects in the form of XR objects by rendering the objects for display.

The XR device 13 may perform the operations above by using a learning model built on at least one or more artificial neural networks. For example, the XR device 13 may recognize physical objects from 3D point cloud or image data by using the learning model and provide information corresponding to the recognized physical objects. Here, the learning model may be the one trained by the XR device 13 itself or trained by an external device such as the AI server 16.

At this time, the XR device 13 may perform the operation by generating a result by employing the learning model directly but also perform the operation by transmitting sensor information to an external device such as the AI server 16 and receiving a result generated accordingly.

<AI+Robot+Autonomous Navigation>

By employing the AI and autonomous navigation technologies, the robot 11 may be implemented as a guide robot, transport robot, cleaning robot, wearable robot, entertainment robot, pet robot, or unmanned flying robot.

The robot 11 employing the AI and autonomous navigation technologies may correspond to a robot itself having an autonomous navigation function or a robot 11 interacting with the self-driving vehicle 12.

The robot 11 having the autonomous navigation function may correspond collectively to the devices which may move autonomously along a given path without control of the user or which may move by determining its path autonomously.

The robot 11 and the self-driving vehicle 12 having the autonomous navigation function may use a common sensing method to determine one or more of the travel path or navigation plan. For example, the robot 11 and the self-driving vehicle 12 having the autonomous navigation function may determine one or more of the travel path or navigation plan by using the information sensed through lidar, radar, and camera.

The robot 11 interacting with the self-driving vehicle 12, which exists separately from the self-driving vehicle 12, may be associated with the autonomous navigation function inside or outside the self-driving vehicle 12 or perform an operation associated with the user riding the self-driving vehicle 12.

At this time, the robot 11 interacting with the self-driving vehicle 12 may obtain sensor information in place of the self-driving vehicle 12 and provide the sensed information to the self-driving vehicle 12; or may control or assist the autonomous navigation function of the self-driving vehicle 12 by obtaining sensor information, generating information of the surroundings or object information, and providing the generated information to the self-driving vehicle 12.

Also, the robot 11 interacting with the self-driving vehicle 12 may control the function of the self-driving vehicle 12 by monitoring the user riding the self-driving vehicle 12 or through interaction with the user. For example, if it is determined that the driver is drowsy, the robot 11 may activate the autonomous navigation function of the self-driving vehicle 12 or assist the control of the driving platform of the self-driving vehicle 12. Here, the function of the self-driving vehicle 12 controlled by the robot 12 may include not only the autonomous navigation function but also the navigation system installed inside the self-driving vehicle 12 or the function provided by the audio system of the self-driving vehicle 12.

Also, the robot 11 interacting with the self-driving vehicle 12 may provide information to the self-driving vehicle 12 or assist functions of the self-driving vehicle 12 from the outside of the self-driving vehicle 12. For example, the robot 11 may provide traffic information including traffic sign information to the self-driving vehicle 12 like a smart traffic light or may automatically connect an electric charger to the charging port by interacting with the self-driving vehicle 12 like an automatic electric charger of the electric vehicle.

<AI+Robot+XR>

By employing the AI technology, the robot 11 may be implemented as a guide robot, transport robot, cleaning robot, wearable robot, entertainment robot, pet robot, or unmanned flying robot.

The robot 11 employing the XR technology may correspond to a robot which acts as a control/interaction target in the XR image. In this case, the robot 11 may be distinguished from the XR device 13, both of which may operate in conjunction with each other.

If the robot 11, which acts as a control/interaction target in the XR image, obtains sensor information from the sensors including a camera, the robot 11 or XR device 13 may generate an XR image based on the sensor information, and the XR device 13 may output the generated XR image. And the robot 11 may operate based on the control signal received through the XR device 13 or based on the interaction with the user.

For example, the user may check the XR image corresponding to the viewpoint of the robot 11 associated remotely through an external device such as the XR device 13, modify the navigation path of the robot 11 through interaction, control the operation or navigation of the robot 11, or check the information of nearby objects.

<AI+Autonomous Navigation+XR>

By employing the AI and XR technologies, the self-driving vehicle 12 may be implemented as a mobile robot, unmanned ground vehicle, or unmanned aerial vehicle.

The self-driving vehicle 12 employing the XR technology may correspond to a self-driving vehicle having a means for providing XR images or a self-driving vehicle which acts as a control/interaction target in the XR image. In particular, the self-driving vehicle 12 which acts as a control/interaction target in the XR image may be distinguished from the XR device 13, both of which may operate in conjunction with each other.

The self-driving vehicle 12 having a means for providing XR images may obtain sensor information from sensors including a camera and output XR images generated based on the sensor information obtained. For example, by displaying an XR image through HUD, the self-driving vehicle 12 may provide XR images corresponding to physical objects or image objects to the passenger.

At this time, if an XR object is output on the HUD, at least part of the XR object may be output so as to be overlapped with the physical object at which the passenger gazes. On the other hand, if an XR object is output on a display installed inside the self-driving vehicle 12, at least part of the XR object may be output so as to be overlapped with an image object. For example, the self-driving vehicle 12 may output XR objects corresponding to the objects such as roads, other vehicles, traffic lights, traffic signs, bicycles, pedestrians, and buildings.

If the self-driving vehicle 12, which acts as a control/interaction target in the XR image, obtains sensor information from the sensors including a camera, the self-driving vehicle 12 or XR device 13 may generate an XR image based on the sensor information, and the XR device 13 may output the generated XR image. And the self-driving vehicle 12 may operate based on the control signal received through an external device such as the XR device 13 or based on the interaction with the user.

[Extended Reality Technology]

eXtended Reality (XR) refers to all of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). The VR technology provides objects or backgrounds of the real world only in the form of CG images, AR technology provides virtual CG images overlaid on the physical object images, and MR technology employs computer graphics technology to mix and merge virtual objects with the real world.

MR technology is similar to AR technology in a sense that physical objects are displayed together with virtual objects. However, while virtual objects supplement physical objects in the AR, virtual and physical objects co-exist as equivalents in the MR.

The XR technology may be applied to Head-Mounted Display (HMD), Head-Up Display (HUD), mobile phone, tablet PC, laptop computer, desktop computer, TV, digital signage, and so on, where a device employing the XR technology may be called an XR device.

A. Example of Block Diagram of UE and 5G Network

FIG. 2 is a block diagram of a wireless communication system to which methods proposed in the disclosure are applicable.

Referring to FIG. 2, a device (AI device) including an AI module is defined as a first communication device (910 of FIG. 2), and a processor 911 can perform detailed AI operation.

A 5G network including another device (AI server) communicating with the AI device is defined as a second communication device (920 of FIG. 2), and a processor 921 can perform detailed AI operations.

The 5G network may be represented as the first communication device and the AI device may be represented as the second communication device.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, an autonomous device, or the like.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, a vehicle, a vehicle having an autonomous function, a connected car, a drone (Unmanned Aerial Vehicle, UAV), and AI (Artificial Intelligence) module, a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, an MR (Mixed Reality) device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a Fin Tech device (or financial device), a security device, a climate/environment device, a device associated with 5G services, or other devices associated with the fourth industrial revolution field.

For example, a terminal or user equipment (UE) may include a cellular phone, a smart phone, a laptop computer, a digital broadcast terminal, personal digital assistants (PDAs), a portable multimedia player (PMP), a navigation device, a slate PC, a tablet PC, an ultrabook, a wearable device (e.g., a smartwatch, a smart glass and a head mounted display (HMD)), etc. For example, the HMD may be a display device worn on the head of a user. For example, the HMD may be used to realize VR, AR or MR. For example, the drone may be a flying object that flies by wireless control signals without a person therein. For example, the VR device may include a device that implements objects or backgrounds of a virtual world. For example, the AR device may include a device that connects and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the MR device may include a device that unites and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the hologram device may include a device that implements 360-degree 3D images by recording and playing 3D information using the interference phenomenon of light that is generated by two lasers meeting each other which is called holography. For example, the public safety device may include an image repeater or an imaging device that can be worn on the body of a user. For example, the MTC device and the IoT device may be devices that do not require direct interference or operation by a person. For example, the MTC device and the IoT device may include a smart meter, a bending machine, a thermometer, a smart bulb, a door lock, various sensors, or the like. For example, the medical device may be a device that is used to diagnose, treat, attenuate, remove, or prevent diseases. For example, the medical device may be a device that is used to diagnose, treat, attenuate, or correct injuries or disorders. For example, the medial device may be a device that is used to examine, replace, or change structures or functions. For example, the medical device may be a device that is used to control pregnancy. For example, the medical device may include a device for medical treatment, a device for operations, a device for (external) diagnose, a hearing aid, an operation device, or the like. For example, the security device may be a device that is installed to prevent a danger that is likely to occur and to keep safety. For example, the security device may be a camera, a CCTV, a recorder, a black box, or the like. For example, the Fin Tech device may be a device that can provide financial services such as mobile payment.

Referring to FIG. 2, the first communication device 910 and the second communication device 920 include processors 911 and 921, memories 914 and 924, one or more Tx/Rx radio frequency (RF) modules 915 and 925, Tx processors 912 and 922, Rx processors 913 and 923, and antennas 916 and 926. The Tx/Rx module is also referred to as a transceiver. Each Tx/Rx module 915 transmits a signal through each antenna 926. The processor implements the aforementioned functions, processes and/or methods. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium. More specifically, the Tx processor 912 implements various signal processing functions with respect to L1 (i.e., physical layer) in DL (communication from the first communication device to the second communication device). The Rx processor implements various signal processing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the first communication device) is processed in the first communication device 910 in a way similar to that described in association with a receiver function in the second communication device 920. Each Tx/Rx module 925 receives a signal through each antenna 926. Each Tx/Rx module provides RF carriers and information to the Rx processor 923. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 3 is a diagram showing an example of a signal transmission/reception method in a wireless communication system.

Referring to FIG. 3, when a UE is powered on or enters a new cell, the UE performs an initial cell search operation such as synchronization with a BS (S201). For this operation, the UE can receive a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the BS to synchronize with the BS and acquire information such as a cell ID. In LTE and NR systems, the P-SCH and S-SCH are respectively called a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). After initial cell search, the UE can acquire broadcast information in the cell by receiving a physical broadcast channel (PBCH) from the BS. Further, the UE can receive a downlink reference signal (DL RS) in the initial cell search step to check a downlink channel state. After initial cell search, the UE can acquire more detailed system information by receiving a physical downlink shared channel (PDSCH) according to a physical downlink control channel (PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radio resource for signal transmission, the UE can perform a random access procedure (RACH) for the BS (steps S203 to S206). To this end, the UE can transmit a specific sequence as a preamble through a physical random access channel (PRACH) (S203 and S205) and receive a random access response (RAR) message for the preamble through a PDCCH and a corresponding PDSCH (S204 and S206). In the case of a contention-based RACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can perform PDCCH/PDSCH reception (S207) and physical uplink shared channel (PUSCH)/physical uplink control channel (PUCCH) transmission (S208) as normal uplink/downlink signal transmission processes. Particularly, the UE receives downlink control information (DCI) through the PDCCH. The UE monitors a set of PDCCH candidates in monitoring occasions set for one or more control element sets (CORESET) on a serving cell according to corresponding search space configurations. A set of PDCCH candidates to be monitored by the UE is defined in terms of search space sets, and a search space set may be a common search space set or a UE-specific search space set. CORESET includes a set of (physical) resource blocks having a duration of one to three OFDM symbols. A network can configure the UE such that the UE has a plurality of CORESETs. The UE monitors PDCCH candidates in one or more search space sets. Here, monitoring means attempting decoding of PDCCH candidate(s) in a search space. When the UE has successfully decoded one of PDCCH candidates in a search space, the UE determines that a PDCCH has been detected from the PDCCH candidate and performs PDSCH reception or PUSCH transmission on the basis of DCI in the detected PDCCH. The PDCCH can be used to schedule DL transmissions over a PDSCH and UL transmissions over a PUSCH. Here, the DCI in the PDCCH includes downlink assignment (i.e., downlink grant (DL grant)) related to a physical downlink shared channel and including at least a modulation and coding format and resource allocation information, or an uplink grant (UL grant) related to a physical uplink shared channel and including a modulation and coding format and resource allocation information.

An initial access (IA) procedure in a 5G communication system will be additionally described with reference to FIG. 3.

The UE can perform cell search, system information acquisition, beam alignment for initial access, and DL measurement on the basis of an SSB. The SSB is interchangeably used with a synchronization signal/physical broadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in four consecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH is transmitted for each OFDM symbol. Each of the PSS and the SSS includes one OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDM symbols and 576 subcarriers.

Cell search refers to a process in which a UE acquires time/frequency synchronization of a cell and detects a cell identifier (ID) (e.g., physical layer cell ID (PCI)) of the cell. The PSS is used to detect a cell ID in a cell ID group and the SSS is used to detect a cell ID group. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group. A total of 1008 cell IDs are present. Information on a cell ID group to which a cell ID of a cell belongs is provided/acquired through an SSS of the cell, and information on the cell ID among 336 cell ID groups is provided/acquired through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity. A default SSB periodicity assumed by a UE during initial cell search is defined as 20 ms. After cell access, the SSB periodicity can be set to one of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., a BS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality of system information blocks (SIBs). SI other than the MIB may be referred to as remaining minimum system information. The MIB includes information/parameter for monitoring a PDCCH that schedules a PDSCH carrying SIB1 (SystemInformationBlock1) and is transmitted by a BS through a PBCH of an SSB. SIB1 includes information related to availability and scheduling (e.g., transmission periodicity and SI-window size) of the remaining SIBs (hereinafter, SIBx, x is an integer equal to or greater than 2). SiBx is included in an SI message and transmitted over a PDSCH. Each SI message is transmitted within a periodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will be additionally described with reference to FIG. 3.

A random access procedure is used for various purposes. For example, the random access procedure can be used for network initial access, handover, and UE-triggered UL data transmission. A UE can acquire UL synchronization and UL transmission resources through the random access procedure. The random access procedure is classified into a contention-based random access procedure and a contention-free random access procedure. A detailed procedure for the contention-based random access procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of a random access procedure in UL. Random access preamble sequences having different two lengths are supported. A long sequence length 839 is applied to subcarrier spacings of 1.25 kHz and 5 kHz and a short sequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz, 60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BS transmits a random access response (RAR) message (Msg2) to the UE. A PDCCH that schedules a PDSCH carrying a RAR is CRC masked by a random access (RA) radio network temporary identifier (RNTI) (RA-RNTI) and transmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UE can receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH. The UE checks whether the RAR includes random access response information with respect to the preamble transmitted by the UE, that is, Msg1. Presence or absence of random access information with respect to Msg1 transmitted by the UE can be determined according to presence or absence of a random access preamble ID with respect to the preamble transmitted by the UE. If there is no response to Msg1, the UE can retransmit the RACH preamble less than a predetermined number of times while performing power ramping. The UE calculates PRACH transmission power for preamble retransmission on the basis of most recent pathloss and a power ramping counter.

The UE can perform UL transmission through Msg3 of the random access procedure over a physical uplink shared channel on the basis of the random access response information. Msg3 can include an RRC connection request and a UE ID. The network can transmit Msg4 as a response to Msg3, and Msg4 can be handled as a contention resolution message on DL. The UE can enter an RRC connected state by receiving Msg4.

C. Beam Management (BM) Procedure of 5G Communication System

A BM procedure can be divided into (1) a DL MB procedure using an SSB or a CSI-RS and (2) a UL BM procedure using a sounding reference signal (SRS). In addition, each BM procedure can include Tx beam swiping for determining a Tx beam and Rx beam swiping for determining an Rx beam.

The DL BM procedure using an SSB will be described.

Configuration of a beam report using an SSB is performed when channel state information (CSI)/beam is configured in RRC_CONNECTED.

-   -   A UE receives a CSI-ResourceConfig IE including         CSI-SSB-ResourceSetList for SSB resources used for BM from a BS.         The RRC parameter “csi-SSB-ResourceSetList” represents a list of         SSB resources used for beam management and report in one         resource set. Here, an SSB resource set can be set as {SSBx1,         SSBx2, SSBx3, SSBx4, . . . }. An SSB index can be defined in the         range of 0 to 63.     -   The UE receives the signals on SSB resources from the BS on the         basis of the CSI-SSB-ResourceSetList.     -   When CSI-RS reportConfig with respect to a report on SSBRI and         reference signal received power (RSRP) is set, the UE reports         the best SSBRI and RSRP corresponding thereto to the BS. For         example, when reportQuantity of the CSI-RS reportConfig IE is         set to ‘ssb-Index-RSRP’, the UE reports the best SSBRI and RSRP         corresponding thereto to the BS.

When a CSI-RS resource is configured in the same OFDM symbols as an SSB and ‘QCL-TypeD’ is applicable, the UE can assume that the CSI-RS and the SSB are quasi co-located (QCL) from the viewpoint of ‘QCL-TypeD’. Here, QCL-TypeD may mean that antenna ports are quasi co-located from the viewpoint of a spatial Rx parameter. When the UE receives signals of a plurality of DL antenna ports in a QCL-TypeD relationship, the same Rx beam can be applied.

Next, a DL BM procedure using a CSI-RS will be described.

An Rx beam determination (or refinement) procedure of a UE and a Tx beam swiping procedure of a BS using a CSI-RS will be sequentially described. A repetition parameter is set to ‘ON’ in the Rx beam determination procedure of a UE and set to ‘OFF’ in the Tx beam swiping procedure of a BS.

First, the Rx beam determination procedure of a UE will be described.

-   -   The UE receives an NZP CSI-RS resource set IE including an RRC         parameter with respect to ‘repetition’ from a BS through RRC         signaling. Here, the RRC parameter ‘repetition’ is set to ‘ON’.     -   The UE repeatedly receives signals on resources in a CSI-RS         resource set in which the RRC parameter ‘repetition’ is set to         ‘ON’ in different OFDM symbols through the same Tx beam (or DL         spatial domain transmission filters) of the BS.     -   The UE determines an RX beam thereof.     -   The UE skips a CSI report. That is, the UE can skip a CSI report         when the RRC parameter ‘repetition’ is set to ‘ON’.

Next, the Tx beam determination procedure of a BS will be described.

-   -   A UE receives an NZP CSI-RS resource set IE including an RRC         parameter with respect to ‘repetition’ from the BS through RRC         signaling. Here, the RRC parameter ‘repetition’ is related to         the Tx beam swiping procedure of the BS when set to ‘OFF’.     -   The UE receives signals on resources in a CSI-RS resource set in         which the RRC parameter ‘repetition’ is set to ‘OFF’ in         different DL spatial domain transmission filters of the BS.     -   The UE selects (or determines) a best beam.     -   The UE reports an ID (e.g., CRI) of the selected beam and         related quality information (e.g., RSRP) to the BS. That is,         when a CSI-RS is transmitted for BM, the UE reports a CRI and         RSRP with respect thereto to the BS.

Next, the UL BM procedure using an SRS will be described.

-   -   A UE receives RRC signaling (e.g., SRS-Config IE) including a         (RRC parameter) purpose parameter set to ‘beam management” from         a BS. The SRS-Config IE is used to set SRS transmission. The         SRS-Config IE includes a list of SRS-Resources and a list of         SRS-ResourceSets. Each SRS resource set refers to a set of         SRS-resources.

The UE determines Tx beamforming for SRS resources to be transmitted on the basis of SRS-SpatialRelation Info included in the SRS-Config IE. Here, SRS-SpatialRelation Info is set for each SRS resource and indicates whether the same beamforming as that used for an SSB, a CSI-RS or an SRS will be applied for each SRS resource.

-   -   When SRS-SpatialRelationInfo is set for SRS resources, the same         beamforming as that used for the SSB, CSI-RS or SRS is applied.         However, when SRS-SpatialRelationInfo is not set for SRS         resources, the UE arbitrarily determines Tx beamforming and         transmits an SRS through the determined Tx beamforming.

Next, a beam failure recovery (BFR) procedure will be described.

In a beamformed system, radio link failure (RLF) may frequently occur due to rotation, movement or beamforming blockage of a UE. Accordingly, NR supports BFR in order to prevent frequent occurrence of RLF. BFR is similar to a radio link failure recovery procedure and can be supported when a UE knows new candidate beams. For beam failure detection, a BS configures beam failure detection reference signals for a UE, and the UE declares beam failure when the number of beam failure indications from the physical layer of the UE reaches a threshold set through RRC signaling within a period set through RRC signaling of the BS. After beam failure detection, the UE triggers beam failure recovery by initiating a random access procedure in a PCell and performs beam failure recovery by selecting a suitable beam. (When the BS provides dedicated random access resources for certain beams, these are prioritized by the UE). Completion of the aforementioned random access procedure is regarded as completion of beam failure recovery.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission defined in NR can refer to (1) a relatively low traffic size, (2) a relatively low arrival rate, (3) extremely low latency requirements (e.g., 0.5 and 1 ms), (4) relatively short transmission duration (e.g., 2 OFDM symbols), (5) urgent services/messages, etc. In the case of UL, transmission of traffic of a specific type (e.g., URLLC) needs to be multiplexed with another transmission (e.g., eMBB) scheduled in advance in order to satisfy more stringent latency requirements. In this regard, a method of providing information indicating preemption of specific resources to a UE scheduled in advance and allowing a URLLC UE to use the resources for UL transmission is provided.

NR supports dynamic resource sharing between eMBB and URLLC. eMBB and URLLC services can be scheduled on non-overlapping time/frequency resources, and URLLC transmission can occur in resources scheduled for ongoing eMBB traffic. An eMBB UE may not ascertain whether PDSCH transmission of the corresponding UE has been partially punctured and the UE may not decode a PDSCH due to corrupted coded bits. In view of this, NR provides a preemption indication. The preemption indication may also be referred to as an interrupted transmission indication.

With regard to the preemption indication, a UE receives DownlinkPreemption IE through RRC signaling from a BS. When the UE is provided with DownlinkPreemption IE, the UE is configured with INT-RNTI provided by a parameter int-RNTI in DownlinkPreemption IE for monitoring of a PDCCH that conveys DCI format 2_1. The UE is additionally configured with a corresponding set of positions for fields in DCI format 2_1 according to a set of serving cells and positionInDCI by INT-ConfigurationPerServing Cell including a set of serving cell indexes provided by servingCellID, configured having an information payload size for DCI format 2_1 according to dci-Payloadsize, and configured with indication granularity of time-frequency resources according to timeFrequencySect.

The UE receives DCI format 2_1 from the BS on the basis of the DownlinkPreemption IE.

When the UE detects DCI format 2_1 for a serving cell in a configured set of serving cells, the UE can assume that there is no transmission to the UE in PRBs and symbols indicated by the DCI format 2_1 in a set of PRBs and a set of symbols in a last monitoring period before a monitoring period to which the DCI format 2_1 belongs. For example, the UE assumes that a signal in a time-frequency resource indicated according to preemption is not DL transmission scheduled therefor and decodes data on the basis of signals received in the remaining resource region.

E. mMTC (Massive MTC)

mMTC (massive Machine Type Communication) is one of 5G scenarios for supporting a hyper-connection service providing simultaneous communication with a large number of UEs. In this environment, a UE intermittently performs communication with a very low speed and mobility. Accordingly, a main goal of mMTC is operating a UE for a long time at a low cost. With respect to mMTC, 3GPP deals with MTC and NB (NarrowBand)-IoT.

mMTC has features such as repetitive transmission of a PDCCH, a PUCCH, a PDSCH (physical downlink shared channel), a PUSCH, etc., frequency hopping, retuning, and a guard period.

That is, a PUSCH (or a PUCCH (particularly, a long PUCCH) or a PRACH) including specific information and a PDSCH (or a PDCCH) including a response to the specific information are repeatedly transmitted. Repetitive transmission is performed through frequency hopping, and for repetitive transmission, (RF) retuning from a first frequency resource to a second frequency resource is performed in a guard period and the specific information and the response to the specific information can be transmitted/received through a narrowband (e.g., 6 resource blocks (RBs) or 1 RB).

FIG. 4A is a block diagram of a mobile terminal in accordance with the disclosure.

The mobile terminal 100 is shown having components such as a wireless communication unit 110, an input unit 120, a sensing unit 140, an output unit 150, an interface unit 160, a memory 170, a controller 180, and a power supply unit 190. It is understood that implementing all of the illustrated components is not a requirement, and that greater or fewer components may alternatively be implemented.

Referring now to FIG. 4A, the mobile terminal 100 is shown having wireless communication unit 110 configured with several commonly implemented components. For instance, the wireless communication unit 110 typically includes one or more components which permit wireless communication between the mobile terminal 100 and a wireless communication system or network within which the mobile terminal is located.

The wireless communication unit 110 typically includes one or more modules which permit communications such as wireless communications between the mobile terminal 100 and a wireless communication system, communications between the mobile terminal 100 and another mobile terminal, communications between the mobile terminal 100 and an external server. Further, the wireless communication unit 110 typically includes one or more modules which connect the mobile terminal 100 to one or more networks. To facilitate such communications, the wireless communication unit 110 includes one or more of a broadcast receiving module 111, a mobile communication module 112, a wireless Internet module 113, a short-range communication module 114, and a location information module 115.

The input unit 120 includes a camera 121 for obtaining images or video, a microphone 122, which is one type of audio input device for inputting an audio signal, and a user input unit 123 (for example, a touch key, a push key, a mechanical key, a soft key, and the like) for allowing a user to input information. Data (for example, audio, video, image, and the like) is obtained by the input unit 120 and may be analyzed and processed by controller 180 according to device parameters, user commands, and combinations thereof.

The sensing unit 140 is typically implemented using one or more sensors configured to sense internal information of the mobile terminal, the surrounding environment of the mobile terminal, user information, and the like. For example, in FIG. 1, the sensing unit 140 is shown having a proximity sensor 141 and an illumination sensor 142.

If desired, the sensing unit 140 may alternatively or additionally include other types of sensors or devices, such as proximity sensor (141), illumination sensor (142), a touch sensor, an acceleration sensor (144), a magnetic sensor, a G-sensor, a gyroscope sensor (143), a motion sensor, an RGB sensor, an infrared (IR) sensor, a force sensor (145), a finger scan sensor, a ultrasonic sensor, an optical sensor (for example, camera 121), a microphone 122, a battery gauge, an environment sensor (for example, a barometer, a hygrometer, a thermometer, a radiation detection sensor, a thermal sensor, and a gas sensor, among others), and a chemical sensor (for example, an electronic nose, a health care sensor, a biometric sensor, and the like), to name a few. The mobile terminal 100 may be configured to utilize information obtained from sensing unit 140, and in particular, information obtained from one or more sensors of the sensing unit 140, and combinations thereof.

The output unit 150 is typically configured to output various types of information, such as audio, video, tactile output, and the like. The output unit 150 is shown having a display unit 151, an audio output module 152, a haptic module 153, and an optical output module 154.

The display unit 151 may have an inter-layered structure or an integrated structure with a touch sensor in order to facilitate a touch screen. The touch screen may provide an output interface between the mobile terminal 100 and a user, as well as function as the user input unit 123 which provides an input interface between the mobile terminal 100 and the user.

The interface unit 160 serves as an interface with various types of external devices that can be coupled to the mobile terminal 100. The interface unit 160, for example, may include any of wired or wireless ports, external power supply ports, wired or wireless data ports, memory card ports, ports for connecting a device having an identification module, audio input/output (I/O) ports, video I/O ports, earphone ports, and the like. In some cases, the mobile terminal 100 may perform assorted control functions associated with a connected external device, in response to the external device being connected to the interface unit 160.

The memory 170 is typically implemented to store data to support various functions or features of the mobile terminal 100. For instance, the memory 170 may be configured to store application programs executed in the mobile terminal 100, data or instructions for operations of the mobile terminal 100, and the like. Some of these application programs may be downloaded from an external server via wireless communication. Other application programs may be installed within the mobile terminal 100 at time of manufacturing or shipping, which is typically the case for basic functions of the mobile terminal 100 (for example, receiving a call, placing a call, receiving a message, sending a message, and the like). It is common for application programs to be stored in the memory 170, installed in the mobile terminal 100, and executed by the controller 180 to perform an operation (or function) for the mobile terminal 100.

The controller 180 typically functions to control overall operation of the mobile terminal 100, in addition to the operations associated with the application programs. The controller 180 may provide or process information or functions appropriate for a user by processing signals, data, information and the like, which are input or output by the various components depicted in FIG. 1, or activating application programs stored in the memory 170. As one example, the controller 180 controls some or all of the components illustrated in FIGS. 1A-1C according to the execution of an application program that have been stored in the memory 170.

The power supply unit 190 can be configured to receive external power or provide internal power in order to supply appropriate power required for operating elements and components included in the mobile terminal 100. The power supply unit 190 may include a battery, and the battery may be configured to be embedded in the terminal body, or configured to be detachable from the terminal body.

Referring still to FIG. 1a , various components depicted in this figure will now be described in more detail. Regarding the wireless communication unit 110, the broadcast receiving module 111 is typically configured to receive a broadcast signal and/or broadcast associated information from an external broadcast managing entity via a broadcast channel. The broadcast channel may include a satellite channel, a terrestrial channel, or both. In some embodiments, two or more broadcast receiving modules 111 may be utilized to facilitate simultaneously receiving of two or more broadcast channels, or to support switching among broadcast channels.

The mobile communication module 112 can transmit and/or receive wireless signals to and from one or more network entities. Typical examples of a network entity include a base station, an external mobile terminal, a server, and the like. Such network entities form part of a mobile communication network, which is constructed according to technical standards or communication methods for mobile communications (for example, Global System for Mobile Communication (GSM), Code Division Multi Access (CDMA), CDMA2000 (Code Division Multi Access 2000), EV-DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only), Wideband CDMA (WCDMA), High Speed Downlink Packet access (HSDPA), HSUPA (High Speed Uplink Packet Access), Long Term Evolution (LTE), LTE-A (Long Term Evolution-Advanced), and the like). Examples of wireless signals transmitted and/or received via the mobile communication module 112 include audio call signals, video (telephony) call signals, or various formats of data to support communication of text and multimedia messages.

The wireless Internet module 113 is configured to facilitate wireless Internet access. This module may be internally or externally coupled to the mobile terminal 100. The wireless Internet module 113 may transmit and/or receive wireless signals via communication networks according to wireless Internet technologies.

Examples of such wireless Internet access include Wireless LAN (WLAN), Wireless Fidelity (Wi-Fi), Wi-Fi Direct, Digital Living Network Alliance (DLNA), Wireless Broadband (WiBro), Worldwide Interoperability for Microwave Access (WiMAX), High Speed Downlink Packet Access (HSDPA), HSUPA (High Speed Uplink Packet Access), Long Term Evolution (LTE), LTE-A (Long Term Evolution-Advanced), and the like. The wireless Internet module 113 may transmit/receive data according to one or more of such wireless Internet technologies, and other Internet technologies as well.

In some embodiments, when the wireless Internet access is implemented according to, for example, WiBro, HSDPA, HSUPA, GSM, CDMA, WCDMA, LTE, LTE-A and the like, as part of a mobile communication network, the wireless Internet module 113 performs such wireless Internet access. As such, the Internet module 113 may cooperate with, or function as, the mobile communication module 112.

The short-range communication module 114 is configured to facilitate short-range communications. Suitable technologies for implementing such short-range communications include BLUETOOTH™, Radio Frequency IDentification (RFID), Infrared Data Association (IrDA), Ultra-WideBand (UWB), ZigBee, Near Field Communication (NFC), Wireless-Fidelity (Wi-Fi), Wi-Fi Direct, Wireless USB (Wireless Universal Serial Bus), and the like. The short-range communication module 114 in general supports wireless communications between the mobile terminal 100 and a wireless communication system, communications between the mobile terminal 100 and another mobile terminal 100, or communications between the mobile terminal and a network where another mobile terminal 100 (or an external server) is located, via wireless area networks. One example of the wireless area networks is a wireless personal area networks.

In some embodiments, another mobile terminal (which may be configured similarly to mobile terminal 100) may be a wearable device, for example, a smart watch, a smart glass or a head mounted display (HMD), which is able to exchange data with the mobile terminal 100 (or otherwise cooperate with the mobile terminal 100). The short-range communication module 114 may sense or recognize the wearable device, and permit communication between the wearable device and the mobile terminal 100. In addition, when the sensed wearable device is a device which is authenticated to communicate with the mobile terminal 100, the controller 180, for example, may cause transmission of data processed in the mobile terminal 100 to the wearable device via the short-range communication module 114. Hence, a user of the wearable device may use the data processed in the mobile terminal 100 on the wearable device. For example, when a call is received in the mobile terminal 100, the user may answer the call using the wearable device. Also, when a message is received in the mobile terminal 100, the user can check the received message using the wearable device.

The location information module 115 is generally configured to detect, calculate, derive or otherwise identify a position of the mobile terminal. As an example, the location information module 115 includes a Global Position System (GPS) module, a Wi-Fi module, or both. If desired, the location information module 115 may alternatively or additionally function with any of the other modules of the wireless communication unit 110 to obtain data related to the position of the mobile terminal.

As one example, when the mobile terminal uses a GPS module, a position of the mobile terminal may be acquired using a signal sent from a GPS satellite. As another example, when the mobile terminal uses the Wi-Fi module, a position of the mobile terminal can be acquired based on information related to a wireless access point (AP) which transmits or receives a wireless signal to or from the Wi-Fi module.

The input unit 120 may be configured to permit various types of input to the mobile terminal 120. Examples of such input include audio, image, video, data, and user input. Image and video input is often obtained using one or more cameras 121. Such cameras 121 may process image frames of still pictures or video obtained by image sensors in a video or image capture mode. The processed image frames can be displayed on the display unit 151 or stored in memory 170. In some cases, the cameras 121 may be arranged in a matrix configuration to permit a plurality of images having various angles or focal points to be input to the mobile terminal 100. As another example, the cameras 121 may be located in a stereoscopic arrangement to acquire left and right images for implementing a stereoscopic image.

The microphone 122 is generally implemented to permit audio input to the mobile terminal 100. The audio input can be processed in various manners according to a function being executed in the mobile terminal 100. If desired, the microphone 122 may include assorted noise removing algorithms to remove unwanted noise generated in the course of receiving the external audio.

The user input unit 123 is a component that permits input by a user. Such user input may enable the controller 180 to control operation of the mobile terminal 100. The user input unit 123 may include one or more of a mechanical input element (for example, a key, a button located on a front and/or rear surface or a side surface of the mobile terminal 100, a dome switch, a jog wheel, a jog switch, and the like), or a touch-sensitive input, among others. As one example, the touch-sensitive input may be a virtual key or a soft key, which is displayed on a touch screen through software processing, or a touch key which is located on the mobile terminal at a location that is other than the touch screen. On the other hand, the virtual key or the visual key may be displayed on the touch screen in various shapes, for example, graphic, text, icon, video, or a combination thereof.

The sensing unit 140 is generally configured to sense one or more of internal information of the mobile terminal, surrounding environment information of the mobile terminal, user information, or the like. The controller 180 generally cooperates with the sending unit 140 to control operation of the mobile terminal 100 or execute data processing, a function or an operation associated with an application program installed in the mobile terminal based on the sensing provided by the sensing unit 140. The sensing unit 140 may be implemented using any of a variety of sensors, some of which will now be described in more detail.

The proximity sensor 141 may include a sensor to sense presence or absence of an object approaching a surface, or an object located near a surface, by using an electromagnetic field, infrared rays, or the like without a mechanical contact. The proximity sensor 141 may be arranged at an inner region of the mobile terminal covered by the touch screen, or near the touch screen.

The proximity sensor 141, for example, may include any of a transmissive type photoelectric sensor, a direct reflective type photoelectric sensor, a mirror reflective type photoelectric sensor, a high-frequency oscillation proximity sensor, a capacitance type proximity sensor, a magnetic type proximity sensor, an infrared rays proximity sensor, and the like. When the touch screen is implemented as a capacitance type, the proximity sensor 141 can sense proximity of a pointer relative to the touch screen by changes of an electromagnetic field, which is responsive to an approach of an object with conductivity. In this case, the touch screen (touch sensor) may also be categorized as a proximity sensor.

The term “proximity touch” will often be referred to herein to denote the scenario in which a pointer is positioned to be proximate to the touch screen without contacting the touch screen. The term “contact touch” will often be referred to herein to denote the scenario in which a pointer makes physical contact with the touch screen. For the position corresponding to the proximity touch of the pointer relative to the touch screen, such position will correspond to a position where the pointer is perpendicular to the touch screen. The proximity sensor 141 may sense proximity touch, and proximity touch patterns (for example, distance, direction, speed, time, position, moving status, and the like).

In general, controller 180 processes data corresponding to proximity touches and proximity touch patterns sensed by the proximity sensor 141, and cause output of visual information on the touch screen. In addition, the controller 180 can control the mobile terminal 100 to execute different operations or process different data according to whether a touch with respect to a point on the touch screen is either a proximity touch or a contact touch.

A touch sensor can sense a touch applied to the touch screen, such as display unit 151, using any of a variety of touch methods. Examples of such touch methods include a resistive type, a capacitive type, an infrared type, and a magnetic field type, among others.

As one example, the touch sensor may be configured to convert changes of pressure applied to a specific part of the display unit 151, or convert capacitance occurring at a specific part of the display unit 151, into electric input signals. The touch sensor may also be configured to sense not only a touched position and a touched area, but also touch pressure and/or touch capacitance. A touch object is generally used to apply a touch input to the touch sensor. Examples of typical touch objects include a finger, a touch pen, a stylus pen, a pointer, or the like.

When a touch input is sensed by a touch sensor, corresponding signals may be transmitted to a touch controller. The touch controller may process the received signals, and then transmit corresponding data to the controller 180. Accordingly, the controller 180 may sense which region of the display unit 151 has been touched. Here, the touch controller may be a component separate from the controller 180, the controller 180, and combinations thereof.

In some embodiments, the controller 180 may execute the same or different controls according to a type of touch object that touches the touch screen or a touch key provided in addition to the touch screen. Whether to execute the same or different control according to the object which provides a touch input may be decided based on a current operating state of the mobile terminal 100 or a currently executed application program, for example.

The touch sensor and the proximity sensor may be implemented individually, or in combination, to sense various types of touches. Such touches include a short (or tap) touch, a long touch, a multi-touch, a drag touch, a flick touch, a pinch-in touch, a pinch-out touch, a swipe touch, a hovering touch, and the like.

If desired, an ultrasonic sensor may be implemented to recognize position information relating to a touch object using ultrasonic waves. The controller 180, for example, may calculate a position of a wave generation source based on information sensed by an illumination sensor and a plurality of ultrasonic sensors. Since light is much faster than ultrasonic waves, the time for which the light reaches the optical sensor is much shorter than the time for which the ultrasonic wave reaches the ultrasonic sensor. The position of the wave generation source may be calculated using this fact. For instance, the position of the wave generation source may be calculated using the time difference from the time that the ultrasonic wave reaches the sensor based on the light as a reference signal.

The camera 121 typically includes at least one a camera sensor (CCD, CMOS etc.), a photo sensor (or image sensors), and a laser sensor.

Implementing the camera 121 with a laser sensor may allow detection of a touch of a physical object with respect to a 3D stereoscopic image. The photo sensor may be laminated on, or overlapped with, the display device. The photo sensor may be configured to scan movement of the physical object in proximity to the touch screen. In more detail, the photo sensor may include photo diodes and transistors at rows and columns to scan content received at the photo sensor using an electrical signal which changes according to the quantity of applied light. Namely, the photo sensor may calculate the coordinates of the physical object according to variation of light to thus obtain position information of the physical object.

The display unit 151 is generally configured to output information processed in the mobile terminal 100. For example, the display unit 151 may display execution screen information of an application program executing at the mobile terminal 100 or user interface (UI) and graphic user interface (GUI) information in response to the execution screen information.

In some embodiments, the display unit 151 may be implemented as a stereoscopic display unit for displaying stereoscopic images. A typical stereoscopic display unit may employ a stereoscopic display scheme such as a stereoscopic scheme (a glass scheme), an auto-stereoscopic scheme (glassless scheme), a projection scheme (holographic scheme), or the like.

The audio output module 152 is generally configured to output audio data. Such audio data may be obtained from any of a number of different sources, such that the audio data may be received from the wireless communication unit 110 or may have been stored in the memory 170. The audio data may be output during modes such as a signal reception mode, a call mode, a record mode, a voice recognition mode, a broadcast reception mode, and the like. The audio output module 152 can provide audible output related to a particular function (e.g., a call signal reception sound, a message reception sound, etc.) performed by the mobile terminal 100. The audio output module 152 may also be implemented as a receiver, a speaker, a buzzer, or the like.

A haptic module 153 can be configured to generate various tactile effects that a user feels, perceive, or otherwise experience. A typical example of a tactile effect generated by the haptic module 153 is vibration. The strength, pattern and the like of the vibration generated by the haptic module 153 can be controlled by user selection or setting by the controller. For example, the haptic module 153 may output different vibrations in a combining manner or a sequential manner.

Besides vibration, the haptic module 153 can generate various other tactile effects, including an effect by stimulation such as a pin arrangement vertically moving to contact skin, a spray force or suction force of air through a jet orifice or a suction opening, a touch to the skin, a contact of an electrode, electrostatic force, an effect by reproducing the sense of cold and warmth using an element that can absorb or generate heat, and the like.

The haptic module 153 can also be implemented to allow the user to feel a tactile effect through a muscle sensation such as the user's fingers or arm, as well as transferring the tactile effect through direct contact. Two or more haptic modules 153 may be provided according to the particular configuration of the mobile terminal 100.

An optical output module 154 can output a signal for indicating an event generation using light of a light source. Examples of events generated in the mobile terminal 100 may include message reception, call signal reception, a missed call, an alarm, a schedule notice, an email reception, information reception through an application, and the like.

A signal output by the optical output module 154 may be implemented in such a manner that the mobile terminal emits monochromatic light or light with a plurality of colors. The signal output may be terminated as the mobile terminal senses that a user has checked the generated event, for example.

The interface unit 160 serves as an interface for external devices to be connected with the mobile terminal 100. For example, the interface unit 160 can receive data transmitted from an external device, receive power to transfer to elements and components within the mobile terminal 100, or transmit internal data of the mobile terminal 100 to such external device. The interface unit 160 may include wired or wireless headset ports, external power supply ports, wired or wireless data ports, memory card ports, ports for connecting a device having an identification module, audio input/output (I/O) ports, video I/O ports, earphone ports, or the like.

The identification module may be a chip that stores various information for authenticating authority of using the mobile terminal 100 and may include a user identity module (UIM), a subscriber identity module (SIM), a universal subscriber identity module (USIM), and the like. In addition, the device having the identification module (also referred to herein as an “identifying device”) may take the form of a smart card. Accordingly, the identifying device can be connected with the terminal 100 via the interface unit 160.

When the mobile terminal 100 is connected with an external cradle, the interface unit 160 can serve as a passage to allow power from the cradle to be supplied to the mobile terminal 100 or may serve as a passage to allow various command signals input by the user from the cradle to be transferred to the mobile terminal there through. Various command signals or power input from the cradle may operate as signals for recognizing that the mobile terminal is properly mounted on the cradle.

The memory 170 can store programs to support operations of the controller 180 and store input/output data (for example, phonebook, messages, still images, videos, etc.). The memory 170 may store data related to various patterns of vibrations and audio which are output in response to touch inputs on the touch screen.

The memory 170 may include one or more types of storage mediums including a Flash memory, a hard disk, a solid state disk, a silicon disk, a multimedia card micro type, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read-Only Memory (ROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Programmable Read-Only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. The mobile terminal 100 may also be operated in relation to a network storage device that performs the storage function of the memory 170 over a network, such as the Internet.

The controller 180 may typically control the general operations of the mobile terminal 100. For example, the controller 180 may set or release a lock state for restricting a user from inputting a control command with respect to applications when a status of the mobile terminal meets a preset condition.

The controller 180 can also perform the controlling and processing associated with voice calls, data communications, video calls, and the like, or perform pattern recognition processing to recognize a handwriting input or a picture drawing input performed on the touch screen as characters or images, respectively. In addition, the controller 180 can control one or a combination of those components in order to implement various exemplary embodiments disclosed herein.

The power supply unit 190 receives external power or provide internal power and supply the appropriate power required for operating respective elements and components included in the mobile terminal 100. The power supply unit 190 may include a battery, which is typically rechargeable or be detachably coupled to the terminal body for charging.

The power supply unit 190 may include a connection port. The connection port may be configured as one example of the interface unit 160 to which an external charger for supplying power to recharge the battery is electrically connected.

As another example, the power supply unit 190 may be configured to recharge the battery in a wireless manner without use of the connection port. In this example, the power supply unit 190 can receive power, transferred from an external wireless power transmitter, using at least one of an inductive coupling method which is based on magnetic induction or a magnetic resonance coupling method which is based on electromagnetic resonance.

Various embodiments described herein may be implemented in a computer-readable medium, a machine-readable medium, or similar medium using, for example, software, hardware, or any combination thereof.

Referring now to FIGS. 4B and 4C, the mobile terminal 100 is described with reference to a bar-type terminal body. However, the mobile terminal 100 may alternatively be implemented in any of a variety of different configurations. Examples of such configurations include watch-type, clip-type, glasses-type, or as a folder-type, flip-type, slide-type, swing-type, and swivel-type in which two and more bodies are combined with each other in a relatively movable manner, and combinations thereof. Discussion herein will often relate to a particular type of mobile terminal (for example, bar-type, watch-type, glasses-type, and the like). However, such teachings with regard to a particular type of mobile terminal will generally apply to other types of mobile terminals as well.

The mobile terminal 100 will generally include a case (for example, frame, housing, cover, and the like) forming the appearance of the terminal. In this embodiment, the case is formed using a front case 101 and a rear case 102. Various electronic components are incorporated into a space formed between the front case 101 and the rear case 102. At least one middle case may be additionally positioned between the front case 101 and the rear case 102.

The display unit 151 is shown located on the front side of the terminal body to output information. As illustrated, a window 151 a of the display unit 151 may be mounted to the front case 101 to form the front surface of the terminal body together with the front case 101.

In some embodiments, electronic components may also be mounted to the rear case 102. Examples of such electronic components include a detachable battery 191, an identification module, a memory card, and the like. Rear cover 103 is shown covering the electronic components, and this cover may be detachably coupled to the rear case 102. Therefore, when the rear cover 103 is detached from the rear case 102, the electronic components mounted to the rear case 102 are externally exposed.

As illustrated, when the rear cover 103 is coupled to the rear case 102, a side surface of the rear case 102 is partially exposed. In some cases, upon the coupling, the rear case 102 may also be completely shielded by the rear cover 103. In some embodiments, the rear cover 103 may include an opening for externally exposing a camera 121 b or an audio output module 152 b.

The cases 101, 102, 103 may be formed by injection-molding synthetic resin or may be formed of a metal, for example, stainless steel (STS), aluminum (Al), titanium (Ti), or the like.

As an alternative to the example in which the plurality of cases form an inner space for accommodating components, the mobile terminal 100 may be configured such that one case forms the inner space. In this example, a mobile terminal 100 having a uni-body is formed in such a manner that synthetic resin or metal extends from a side surface to a rear surface.

If desired, the mobile terminal 100 may include a waterproofing unit (not shown) for preventing introduction of water into the terminal body. For example, the waterproofing unit may include a waterproofing member which is located between the window 151 a and the front case 101, between the front case 101 and the rear case 102, or between the rear case 102 and the rear cover 103, to hermetically seal an inner space when those cases are coupled.

The mobile terminal includes a display unit 151, a first and a second audio output modules 151 a/151 b, a proximity sensor 141, an illumination sensor 142, an optical output module 154, a first and a second cameras 121 a/121 b, a first and a second manipulation units 123 a/123 b, a microphone 122, interface unit 160 and the like.

It will be described for the mobile terminal as shown in FIGS. 1B and 1C. The display unit 151, the first audio output module 151 a, the proximity sensor 141, an illumination sensor 142, the optical output module 154, the first camera 121 a and the first manipulation unit 123 a are arranged in front surface of the terminal body, the second manipulation unit 123 b, the microphone 122 and interface unit 160 are arranged in side surface of the terminal body, and the second audio output modules 151 b and the second camera 121 b are arranged in rear surface of the terminal body.

However, it is to be understood that alternative arrangements are possible and within the teachings of the instant disclosure. Some components may be omitted or rearranged. For example, the first manipulation unit 123 a may be located on another surface of the terminal body, and the second audio output module 152 b may be located on the side surface of the terminal body.

The display unit 151 outputs information processed in the mobile terminal 100. The display unit 151 may be implemented using one or more suitable display devices. Examples of such suitable display devices include a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT-LCD), an organic light emitting diode (OLED), a flexible display, a 3-dimensional (3D) display, an e-ink display, and combinations thereof.

The display unit 151 may be implemented using two display devices, which can implement the same or different display technology. For instance, a plurality of the display units 151 may be arranged on one side, either spaced apart from each other, or these devices may be integrated, or these devices may be arranged on different surfaces.

The display unit 151 may also include a touch sensor which senses a touch input received at the display unit. When a touch is input to the display unit 151, the touch sensor may be configured to sense this touch and the controller 180, for example, may generate a control command or other signal corresponding to the touch. The content which is input in the touching manner may be a text or numerical value, or a menu item which can be indicated or designated in various modes.

The touch sensor may be configured in a form of a film having a touch pattern, disposed between the window 151 a and a display on a rear surface of the window 151 a, or a metal wire which is patterned directly on the rear surface of the window 151 a. Alternatively, the touch sensor may be integrally formed with the display. For example, the touch sensor may be disposed on a substrate of the display or within the display.

The display unit 151 may also form a touch screen together with the touch sensor. Here, the touch screen may serve as the user input unit 123 (see FIG. 1A). Therefore, the touch screen may replace at least some of the functions of the first manipulation unit 123 a.

The first audio output module 152 a may be implemented in the form of a speaker to output voice audio, alarm sounds, multimedia audio reproduction, and the like.

The window 151 a of the display unit 151 will typically include an aperture to permit audio generated by the first audio output module 152 a to pass. One alternative is to allow audio to be released along an assembly gap between the structural bodies (for example, a gap between the window 151 a and the front case 101). In this case, a hole independently formed to output audio sounds may not be seen or is otherwise hidden in terms of appearance, thereby further simplifying the appearance and manufacturing of the mobile terminal 100.

The optical output module 154 can be configured to output light for indicating an event generation. Examples of such events include a message reception, a call signal reception, a missed call, an alarm, a schedule notice, an email reception, information reception through an application, and the like. When a user has checked a generated event, the controller can control the optical output unit 154 to stop the light output.

The first camera 121 a can process image frames such as still or moving images obtained by the image sensor in a capture mode or a video call mode. The processed image frames can then be displayed on the display unit 151 or stored in the memory 170.

The first and second manipulation units 123 a and 123 b are examples of the user input unit 123, which may be manipulated by a user to provide input to the mobile terminal 100. The first and second manipulation units 123 a and 123 b may also be commonly referred to as a manipulating portion, and may employ any tactile method that allows the user to perform manipulation such as touch, push, scroll, or the like. The first and second manipulation units 123 a and 123 b may also employ any non-tactile method that allows the user to perform manipulation such as proximity touch, hovering, or the like.

FIG. 4B illustrates the first manipulation unit 123 a as a touch key, but possible alternatives include a mechanical key, a push key, a touch key, and combinations thereof.

Input received at the first and second manipulation units 123 a and 123 b may be used in various ways. For example, the first manipulation unit 123 a may be used by the user to provide an input to a menu, home key, cancel, search, or the like, and the second manipulation unit 123 b may be used by the user to provide an input to control a volume level being output from the first or second audio output modules 152 a or 152 b, to switch to a touch recognition mode of the display unit 151, or the like.

As another example of the user input unit 123, a rear input unit (not shown) may be located on the rear surface of the terminal body. The rear input unit can be manipulated by a user to provide input to the mobile terminal 100. The input may be used in a variety of different ways. For example, the rear input unit may be used by the user to provide an input for power on/off, start, end, scroll, control volume level being output from the first or second audio output modules 152 a or 152 b, switch to a touch recognition mode of the display unit 151, and the like. The rear input unit may be configured to permit touch input, a push input, or combinations thereof.

The rear input unit may be located to overlap the display unit 151 of the front side in a thickness direction of the terminal body. As one example, the rear input unit may be located on an upper end portion of the rear side of the terminal body such that a user can easily manipulate it using a forefinger when the user grabs the terminal body with one hand. Alternatively, the rear input unit can be positioned at most any location of the rear side of the terminal body.

Embodiments that include the rear input unit may implement some or all of the functionality of the first manipulation unit 123 a in the rear input unit. As such, in situations where the first manipulation unit 123 a is omitted from the front side, the display unit 151 can have a larger screen.

As a further alternative, the mobile terminal 100 may include a finger scan sensor which scans a user's fingerprint. The controller 180 can then use fingerprint information sensed by the finger scan sensor as part of an authentication procedure. The finger scan sensor may also be installed in the display unit 151 or implemented in the user input unit 123.

The microphone 122 is shown located at an end of the mobile terminal 100, but other locations are possible. If desired, multiple microphones may be implemented, with such an arrangement permitting the receiving of stereo sounds.

The interface unit 160 may serve as a path allowing the mobile terminal 100 to interface with external devices. For example, the interface unit 160 may include one or more of a connection terminal for connecting to another device (for example, an earphone, an external speaker, or the like), a port for near field communication (for example, an Infrared Data Association (IrDA) port, a Bluetooth port, a wireless LAN port, and the like), or a power supply terminal for supplying power to the mobile terminal 100. The interface unit 160 may be implemented in the form of a socket for accommodating an external card, such as Subscriber Identification Module (SIM), User Identity Module (UIM), or a memory card for information storage.

The second camera 121 b is shown located at the rear side of the terminal body and includes an image capturing direction that is substantially opposite to the image capturing direction of the first camera unit 121 a. If desired, second camera 121 a may alternatively be located at other locations, or made to be moveable, in order to have a different image capturing direction from that which is shown.

The second camera 121 b can include a plurality of lenses arranged along at least one line. The plurality of lenses may also be arranged in a matrix configuration. The cameras may be referred to as an “array camera.” When the second camera 121 b is implemented as an array camera, images may be captured in various manners using the plurality of lenses and images with better qualities.

As shown in FIG. 4C, a flash 124 is shown adjacent to the second camera 121 b. When an image of a subject is captured with the camera 121 b, the flash 124 may illuminate the subject.

As shown in FIG. 4B, the second audio output module 152 b can be located on the terminal body. The second audio output module 152 b may implement stereophonic sound functions in conjunction with the first audio output module 152 a, and may be also used for implementing a speaker phone mode for call communication.

At least one antenna for wireless communication may be located on the terminal body. The antenna may be installed in the terminal body or formed by the case. For example, an antenna which configures a part of the broadcast receiving module 111 may be retractable into the terminal body. Alternatively, an antenna may be formed using a film attached to an inner surface of the rear cover 103, or a case that includes a conductive material.

A power supply unit 190 for supplying power to the mobile terminal 100 may include a battery 191, which is mounted in the terminal body or detachably coupled to an outside of the terminal body. The battery 191 may receive power via a power source cable connected to the interface unit 160. Also, the battery 191 can be recharged in a wireless manner using a wireless charger. Wireless charging may be implemented by magnetic induction or electromagnetic resonance.

The rear cover 103 is shown coupled to the rear case 102 for shielding the battery 191, to prevent separation of the battery 191, and to protect the battery 191 from an external impact or from foreign material. When the battery 191 is detachable from the terminal body, the rear case 103 may be detachably coupled to the rear case 102.

An accessory for protecting an appearance or assisting or extending the functions of the mobile terminal 100 can also be provided on the mobile terminal 100. As one example of an accessory, a cover or pouch for covering or accommodating at least one surface of the mobile terminal 100 may be provided. The cover or pouch may cooperate with the display unit 151 to extend the function of the mobile terminal 100. Another example of the accessory is a touch pen for assisting or extending a touch input to a touch screen.

F. Basic Operation of AI Processing Using 5G Communication

FIG. 5 shows an example of basic operations of AI processing in a 5G communication system.

The UE transmits specific information to the 5G network (S1). The 5G network may perform 5G processing related to the specific information (S2). Here, the 5G processing may include AI processing. And the 5G network may transmit response including AI processing result to UE (S3).

G. Applied Operations Between UE and 5G Network in 5G Communication System

Hereinafter, the operation of an autonomous vehicle using 5G communication will be described in more detail with reference to wireless communication technology (BM procedure, URLLC, mMTC, etc.) described in FIG. 5.

First, a basic procedure of an applied operation to which a method proposed by the disclosure which will be described later and eMBB of 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 5, the autonomous vehicle performs an initial access procedure and a random access procedure with the 5G network prior to step S1 of FIG. 3 in order to transmit/receive signals, information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial access procedure with the 5G network on the basis of an SSB in order to acquire DL synchronization and system information. A beam management (BM) procedure and a beam failure recovery procedure may be added in the initial access procedure, and quasi-co-location (QCL) relation may be added in a process in which the autonomous vehicle receives a signal from the 5G network.

In addition, the autonomous vehicle performs a random access procedure with the 5G network for UL synchronization acquisition and/or UL transmission. The 5G network can transmit, to the autonomous vehicle, a UL grant for scheduling transmission of specific information. Accordingly, the autonomous vehicle transmits the specific information to the 5G network on the basis of the UL grant. In addition, the 5G network transmits, to the autonomous vehicle, a DL grant for scheduling transmission of 5G processing results with respect to the specific information. Accordingly, the 5G network can transmit, to the autonomous vehicle, information (or a signal) related to remote control on the basis of the DL grant.

Next, a basic procedure of an applied operation to which a method proposed by the disclosure which will be described later and URLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemption IE from the 5G network after the autonomous vehicle performs an initial access procedure and/or a random access procedure with the 5G network. Then, the autonomous vehicle receives DCI format 2_1 including a preemption indication from the 5G network on the basis of DownlinkPreemption IE. The autonomous vehicle does not perform (or expect or assume) reception of eMBB data in resources (PRBs and/or OFDM symbols) indicated by the preemption indication. Thereafter, when the autonomous vehicle needs to transmit specific information, the autonomous vehicle can receive a UL grant from the 5G network.

Next, a basic procedure of an applied operation to which a method proposed by the disclosure which will be described later and mMTC of 5G communication are applied will be described.

Description will focus on parts in the steps of FIG. 3 which are changed according to application of mMTC.

In step S1 of FIG. 5, the autonomous vehicle receives a UL grant from the 5G network in order to transmit specific information to the 5G network. Here, the UL grant may include information on the number of repetitions of transmission of the specific information and the specific information may be repeatedly transmitted on the basis of the information on the number of repetitions. That is, the autonomous vehicle transmits the specific information to the 5G network on the basis of the UL grant. Repetitive transmission of the specific information may be performed through frequency hopping, the first transmission of the specific information may be performed in a first frequency resource, and the second transmission of the specific information may be performed in a second frequency resource. The specific information can be transmitted through a narrowband of 6 resource blocks (RBs) or 1 RB.

The above-described 5G communication technology can be combined with methods proposed in the disclosure which will be described later and applied or can complement the methods proposed in the disclosure to make technical features of the methods concrete and clear.

The previously described 5G communication technology may be applied in combination with the methods proposed in the specification, which will be described later, or may be supplemented to specify or clarify the technical characteristics of the methods proposed in the specification.

FIG. 6 is a block diagram of an AI device according to an embodiment of the disclosure.

An AI device 20 may include an electronic device including an AI module that can perform AI processing, a server including the AI module, or the like. Further, the AI device 20 may be included as at least one component of the vehicle 10 shown in FIG. 2 to perform together at least a portion of the AI processing.

The AI processing may include all operations related to driving of the vehicle 10 shown in FIG. 6. For example, an autonomous vehicle can perform operations of processing/determining, and control signal generating by performing AI processing on sensing data or driver data. Further, for example, an autonomous vehicle can perform autonomous driving control by performing AI processing on data acquired through interaction with other electronic devices included in the vehicle.

The AI device 20 may include an AI processor 21, a memory 25, and/or a communication unit 27.

The AI device 20, which is a computing device that can learn a neural network, may be implemented as various electronic devices such as a server, a desktop PC, a notebook PC, and a tablet PC.

The AI processor 21 can learn a neural network using programs stored in the memory 25. In particular, the AI processor 21 can learn a neural network for recognizing data related to vehicles. Here, the neural network for recognizing data related to vehicles may be designed to simulate the brain structure of human on a computer and may include a plurality of network nodes having weights and simulating the neurons of human neural network. The plurality of network nodes can transmit and receive data in accordance with each connection relationship to simulate the synaptic activity of neurons in which neurons transmit and receive signals through synapses. Here, the neural network may include a deep learning model developed from a neural network model. In the deep learning model, a plurality of network nodes is positioned in different layers and can transmit and receive data in accordance with a convolution connection relationship. The neural network, for example, includes various deep learning techniques such as deep neural networks (DNN), convolutional deep neural networks (CNN), recurrent neural networks (RNN), a restricted Boltzmann machine (RBM), deep belief networks (DBN), and a deep Q-network, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.

Meanwhile, a processor that performs the functions described above may be a general purpose processor (e.g., a CPU), but may be an AI-only processor (e.g., a GPU) for artificial intelligence learning.

The memory 25 can store various programs and data for the operation of the AI device 20. The memory 25 may be a nonvolatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), a solid state drive (SDD), or the like. The memory 25 is accessed by the AI processor 21 and reading-out/recording/correcting/deleting/updating, etc. of data by the AI processor 21 can be performed. Further, the memory 25 can store a neural network model (e.g., a deep learning model 26) generated through a learning algorithm for data classification/recognition according to an embodiment of the disclosure.

Meanwhile, the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition. The data learning unit 22 can learn references about what learning data are used and how to classify and recognize data using the learning data in order to determine data classification/recognition. The data learning unit 22 can learn a deep learning model by acquiring learning data to be used for learning and by applying the acquired learning data to the deep learning model.

The data learning unit 22 may be manufactured in the type of at least one hardware chip and mounted on the AI device 20. For example, the data learning unit 22 may be manufactured in a hardware chip type only for artificial intelligence, and may be manufactured as a part of a general purpose processor (CPU) or a graphics processing unit (GPU) and mounted on the AI device 20. Further, the data learning unit 22 may be implemented as a software module. When the data leaning unit 22 is implemented as a software module (or a program module including instructions), the software module may be stored in non-transitory computer readable media that can be read through a computer. In this case, at least one software module may be provided by an OS (operating system) or may be provided by an application.

The data learning unit 22 may include a learning data acquiring unit 23 and a model learning unit 24.

The learning data acquiring unit 23 can acquire learning data required for a neural network model for classifying and recognizing data. For example, the learning data acquiring unit 23 can acquire, as learning data, vehicle data and/or sample data to be input to a neural network model.

The model learning unit 24 can perform learning such that a neural network model has a determination reference about how to classify predetermined data, using the acquired learning data. In this case, the model learning unit 24 can train a neural network model through supervised learning that uses at least some of learning data as a determination reference. Alternatively, the model learning data 24 can train a neural network model through unsupervised learning that finds out a determination reference by performing learning by itself using learning data without supervision. Further, the model learning unit 24 can train a neural network model through reinforcement learning using feedback about whether the result of situation determination according to learning is correct. Further, the model learning unit 24 can train a neural network model using a learning algorithm including error back-propagation or gradient decent.

When a neural network model is learned, the model learning unit 24 can store the learned neural network model in the memory. The model learning unit 24 may store the learned neural network model in the memory of a server connected with the AI device 20 through a wire or wireless network.

The data learning unit 22 may further include a learning data preprocessor (not shown) and a learning data selector (not shown) to improve the analysis result of a recognition model or reduce resources or time for generating a recognition model.

The learning data preprocessor can preprocess acquired data such that the acquired data can be used in learning for situation determination. For example, the learning data preprocessor can process acquired data in a predetermined format such that the model learning unit 24 can use learning data acquired for learning for image recognition.

Further, the learning data selector can select data for learning from the learning data acquired by the learning data acquiring unit 23 or the learning data preprocessed by the preprocessor. The selected learning data can be provided to the model learning unit 24. For example, the learning data selector can select only data for objects included in a specific area as learning data by detecting the specific area in an image acquired through a camera of a vehicle.

Further, the data learning unit 22 may further include a model estimator (not shown) to improve the analysis result of a neural network model.

The model estimator inputs estimation data to a neural network model, and when an analysis result output from the estimation data does not satisfy a predetermined reference, it can make the model learning unit 22 perform learning again. In this case, the estimation data may be data defined in advance for estimating a recognition model. For example, when the number or ratio of estimation data with an incorrect analysis result of the analysis result of a recognition model learned with respect to estimation data exceeds a predetermined threshold, the model estimator can estimate that a predetermined reference is not satisfied.

The communication unit 27 can transmit the AI processing result by the AI processor 21 to an external electronic device.

Here, the external electronic device may be defined as an autonomous vehicle. Further, the AI device 20 may be defined as another vehicle or a 5G network that communicates with the autonomous vehicle. Meanwhile, the AI device 20 may be implemented by being functionally embedded in an autonomous module included in a vehicle. Further, the 5G network may include a server or a module that performs control related to autonomous driving.

Meanwhile, the AI device 20 shown in FIG. 6 was functionally separately described into the AI processor 21, the memory 25, the communication unit 27, etc., but it should be noted that the aforementioned components may be integrated in one module and referred to as an AI module.

FIG. 7 is a flowchart illustrating a method for controlling an electronic device according to an embodiment of the disclosure.

The method for controlling an electronic device, illustrated in FIG. 7, may be implemented by the controller of FIG. 4A or may be implemented as the controller instructs the processor to control. Hereinafter, in the disclosure, the controller and the processor are assumed to perform the same function.

Referring to FIG. 7, a processor 180 of an electronic device 100 records a video via a camera (S700).

The electronic device may include at least one camera equipped in a mobile terminal. The electronic device may include a monitoring camera system equipped indoors and may not be limited to a specific one as long as it is a video recording device. Meanwhile, according to an embodiment of the disclosure, the electronic device performing audio zoom and the camera may be separated from each other. According to an embodiment, after an image recorded by the camera system is transferred to the electronic device via a wireless communication unit, the audio zoom function disclosed herein may be implemented.

The processor 180 may obtain a sound via at least one microphone while recording the video (S710).

The processor 180 may detect the sound source direction of the obtained sound via predetermined signal processing (S720).

Here, the predetermined signal processing may mean a process for determining the position (e.g., phase or direction in space) of the sound signal using the correlation between a plurality of sound signals or the correlation between a plurality of noise signals.

According to an embodiment of the disclosure, detection of the sound source direction while recording the video may be implemented by various methods.

According to an embodiment, the electronic device 100 may include a plurality of microphones and may adopt a beamforming technique to allow a beam to be formed in a specific direction using the plurality of microphones. For example, where a beamforming scheme using a plurality of microphones applies, the mobile terminal 100 may form a first beam and a second beam and may perform control so that the first beam may be formed forward of the plurality of microphones, and the second beam may be formed to the left or right with respect to the first beam. The electronic device 100 may extract a speech signal using a difference between an audio signal received via the second beam and an audio signal received via the first beam. In this case, noise signals may be removed by subtracting the audio signal received via the first beam from the audio signals received via the first beam and the second beam.

Further, according to an embodiment of the disclosure, an adaptive digital filter-applied beamforming method may apply. The plurality of microphones included in the electronic device 100 may be divided into a main microphone and a sub microphone and may be configured so that the signal resultant from subtracting signals from the sub microphone from signals from the main microphone is input to the adaptive digital filter (ADF).

If the sound source direction is detected, the processor 180 may determine whether the sound contains a human speech.

According to an embodiment, the processor 180 may determine (or estimate) whether a speech signal is present in the input signal or the probability of presence of the speech signal. For example, the processor 180 may determine a segment of a specific frequency band, where a speech signal exists, using a speech presence probability (SPP) estimation algorithm used for noise suppression (NSA) for one channel or a complex Gaussian mixture model (CGMM)-based estimation algorithm used for NS for a plurality of channels.

The processor 180 may recognize the face and mouth shape of a person positioned in the sound source direction (S730) and recognize the speaker who is issuing an utterance via lip reading analysis (S740).

The processor 180 may recognize the person's face by applying the input image to a pre-trained neural network model. The neural network model may recognize the position of a landmark, such as eyes, nose, or mouth, in the recognized face, as well as the face in the image according to the kind of training data.

Further, the processor 180 may detect the position of the mouth in the recognized human face based on the image captured via the camera. Further, the processor 180 may determine whether the person recognized according to the movement of the lips is issuing an utterance by reading the lip movement.

According to an embodiment, if the gap between the upper and lower lips is a predetermined size or more according to the movement of the lips, the processor 180 may determine that the speaker is issuing an utterance. Further, according to an embodiment, whether the speaker is issuing an utterance may be determined using a neural network model trained to be able to detect feature points of the lips of the recognized person and estimate whether he issues an utterance and/or the result of speech recognition according to the utterance, according to variations in position of the feature points. Further, according to an embodiment, feature points of the lips of the recognized person may be detected, and variations in position of the feature points of the lips may be determined to be present within a predetermined time gap, based on the video frames input in the predetermined time gap. If the variations in position of the feature points occur in a predetermined number of times or more, it may be determined that there is the speaker's utterance. When the speaker yawns, a variation occurs in the position of the feature points of the lips. However, only when a predetermined number of, or more, variations in position of the feature points occur, there may be determined to be an utterance.

The processor 180 may perform beamforming of the microphone based on the speaker's direction to thereby execute an audio zoom function (S750).

The beamforming operation may mean that the electronic device 100 applies a weight to control the directivity of the input sound signal. The electronic device 100 may attenuate the noise signals contained in the sound signals by applying weights determined by a predetermined parameter to the sound signals. According to an embodiment, the weight used to perform the beamforming operation may be determined by a DNN model.

The beamforming operation may be implemented by a predetermined signal processing module. The signal processing module may include a software or hardware module to perform a series of operations for performing beamforming. The signal processing module may be embedded in the microphone and be controlled by the processor 180.

According to an embodiment, after recognizing the speaker's direction and determining the direction of beamforming, the processor 180 may adjust the gain of the microphone in the beamforming direction after recognizing the speaker's utterance. For example, although the electronic device 100 recognizes the speaker, the processor 180 may keep the microphone's reaction sensitivity low while the speaker issues no utterance and, from the time when the speaker's utterance is recognized, keeps the microphone's reaction sensitivity high, thereby maximizing the steered power of the beam former, as necessary, and hence allowing for efficient operation of resources.

Further, according to an embodiment, the processor 180 may efficiently perform audio zoom by adjusting the gain of the microphone depending on the distance between the electronic device 100 and the speaker being recorded via the camera.

Further, according to an embodiment, the processor 180 may perform audio zoom by adjusting the gain of the microphone depending on the number of persons included in the video being recorded. For example, the microphone's beamforming may be carried out so that only noise signals distinct from the sound signals are removed upon recording a lecture where there are one speaker and an audience, or there may be no need for maintaining the maximum gain of microphone.

FIG. 8 is a view illustrating S730 of FIG. 7.

Referring to FIG. 8, if the sound source direction is detected, the processor 180 may use information for the detected sound source direction in facial recognition. For example, there may be a plurality of objects P1, P2, and P3 in the video recording screen MP, and a first area A1 positioned in the sound source direction may have the first object P1 and the second object P2.

The processor 180 may apply a plurality of video frames IF₁, IF₂, . . . , IF_(n) as inputs to the artificial neural network (ANN) and perform control to perform image recognition (facial recognition) via the ANN. The outputs from the ANN may include a facial image of the first object P1 and a facial image of the second object P2. Where the ANN is a model trained to recognize the eyes, nose, and mouth in the face, as well as the face of the object, an image may be output in which even the shapes of the eyes 810, nose 820, and mouth 830 in the face of the first object P1 may be rendered distinct.

FIG. 9 is a view illustrating an example of detecting a speaker's utterance based on a variation in the mouth shape based on facial recognition.

Referring to FIG. 9, even the position of the mouth may be identified in the face recognized via an ANN. The ANN may be a model trained to detect variations in the shape of the mouth. The ANN for facial recognition may detect a person's lips and extract feature points F1, F2, . . . , F10 from the detected lips.

The ANN may receive a video frame recorded during a predetermined time as an input value, analyze the frame, and output a result as to whether the speaker is issuing an utterance or not. Here, the ANN may determine whether there is a variation in the shape of the speaker's mouth during a predetermined time.

According to an embodiment, for the variation in the shape of the speaker's mouth, lip reading which is used in speech recognition fields may be put to use. To that end, the processor 180 may extract features necessary for recognition from the captured image. A method for extracting features from an image may be a model-based method that extracts the contour of the lips using a geometric model and uses a coefficient of the extracted lip model as a feature value. Further, a pixel-based method may apply which extracts the lips area from the input image and reduces the size of the features by applying main component analysis, linear determination analysis, and discrete cosine transform or other transform to the pixel values.

According to an embodiment, the ANN may detect lip features from the input image. The ANN may be a model trained to perform the function of an active appearance model (AAM) lip model. A shape or appearance model may be created using the model. After the lips are detected, lip feature points obtained as a result of AAM fitting may be traced from the image using a pre-created shape model and a predetermined algorithm. As a result of tracing the lips for continuous lip images, a series of lip model parameters and frame data for a predetermined interval may be input to the ANN, thereby determining whether the speaker is uttering or silent during the interval.

FIGS. 10A, 10B, and 10C are views illustrating the orientation characteristics of a microphone according to an embodiment of the disclosure.

FIG. 10A illustrates the sensitivity and radius of an omni-directional microphone. Referring to FIG. 10A, the omni-directional microphone has the characteristics of reacting to sounds obtained at all angles. Here, all angles means that the microphone reacts, in the same manner, to sounds obtained in zero-degree to 180-degree directions.

FIG. 10B illustrates the sensitivity and radius of a uni-directional microphone. Referring to FIG. 10B, the uni-directional microphone may sensitively react only to sounds obtained from a specific direction. The microphone may react only to sounds from the front in a zero-degree direction and from a side (in a 90-degree direction) and may be insensitive to sounds from the back of the electronic device.

FIG. 10C illustrates the sensitivity and radius of a super-directional microphone. Referring to FIG. 10C, the microphone may sensitively react only to sounds obtained from the front in a small angle, e.g., 30-degree direction while having no or little reaction to sounds obtained in other angles.

According to an embodiment of the disclosure, the processor 180 may amplify speeches generated from the direction of the speaker while reducing sounds from other directions than the speaker's direction, using a plurality of microphones with different directional characteristics of FIGS. 10A to 10C.

According to an embodiment, upon recognizing a change in the position of the speaker, the processor 180 may control to perform a microphone beamforming operation based on the changed position. For example, selective use of a plurality of microphones with different directional characteristics enables an efficient audio zoom on the speaker's speeches in the changed position.

According to an embodiment of the disclosure, the processor 180 may identify a change in the position of the speaker via detection of the sound source direction. However, such an occasion may exist where, after specified as the speaker, the speaker changes his position without uttering. In such a case, the processor 180 may control the audio zoom according to the speaker's repositioning by detecting real-time changes in the position of the specified speaker in the image captured by the camera.

FIG. 11 is a flowchart illustrating a method for controlling an electronic device according to another embodiment of the disclosure.

Referring to FIG. 11, the processor 180 of the electronic device 100 may record a video via a camera (S1100).

The electronic device 100 may include a plurality of microphones. The processor 180 may obtain a sound while recording the video via the plurality of microphones (S1110). The electronic device 100 with the plurality of microphones may split the sound sources in the number of microphones.

Further, the processor 180 may detect the sound source direction for the obtained sound (S1120). S1100 to S1120 are the same as those described above in connection with FIG. 7.

The processor 180 may determine whether the obtained sounds contain human speeches. The processor 180 may also determine whether the human speeches come from a plurality of people (S1130). Upon determining that there are a plurality of speeches, the processor 180 may split the speeches per speaker, using the plurality of microphones (S1140).

Typically, if N microphones receive predetermined speech signals, they may be split into N sound sources. Thus, the number of the sound sources split into may correspond to the number of the speakers. Where the electronic device 100 has as many microphones as the number of the speakers, sounds obtained via the microphones are split into as many sound sources as the number of microphones, and human facial and mouth shape recognition may be performed based on the direction of each sound source.

However, the number of the microphones of the electronic device 100 may differ from the number of speakers.

If the number of sound sources is larger than the number of microphones (Y in S1150), the processor 180 may group at least two or more of the plurality of speakers, thereby allowing each of the plurality of microphones to collect sounds from the same number of speakers. For example, if the number of the sound sources is larger than the number of the microphones, the processor 180 may calculate the distances between the plurality of speakers in the captured image. The processor 180 may group the speakers for which the inter-speaker distance is short and perform audio zoom to allow the beamforming direction of one microphone for the speakers in the group to cover the entire group.

Meanwhile, if the number of the sound sources is smaller than the number of the microphones (N in S1150), the processor 180 may control to allow at least two or more microphones to have the same beamforming direction. The processor 180 may control to allow at least two or more of the plurality of microphones to be oriented in the same beamforming direction according to a predetermined criterion. For example, the predetermined criterion may be the inter-speaker distance. Where the uttering times of the speakers who are close to each other (are positioned in a short distance) at least partially overlap, it may be impossible to split sound sources using one microphone. Thus, if the number of the microphones is larger than the number of the speakers, the processor 180 may control the microphones to allow at least two or more microphones to perform beamforming in the direction of the same speaker group, for the speeches uttered from one speaker or two or more speakers. In other words, the number of the speaker group determined based on a distance between the plurality of persons issuing utterances when the number of sound sources is larger than the number of the microphones. For example, the number of the speaker group may be equal to the number of the microphones. Then, the processor 180 may perform beamforming in a direction of the speaker groups.

FIG. 12 is a flowchart illustrating a method for controlling an electronic device according to an embodiment of the disclosure.

Referring to FIG. 12, the processor 180 may recognize the sound source direction of the sound obtained while recording a video via the camera (S1200).

The processor 180 may recognize the face of a person in the area corresponding to the recognized sound source direction. Further, the processor 180 may recognize a variation in mouth shape in the person's face (S1210). Upon recognizing that the person recognized via lip reading processing is uttering, the processor 180 may determine that there is a speaker and identify the speaker's utterance.

Recognition of the person's face and a variation in mouth shape may be detected by applying a DNN model as set forth above.

The processor 180 may control microphone beamforming based on the speaker's direction and perform speech recognition on the input audio signal (S1220).

The processor 180 may store text corresponding to the result of speech recognition, along with the recorded video, in the memory. The processor 180 may perform a natural language process on the received audio signal to convert the speaker's intended utterance into text, and store the text together with the video (S1230). According to an embodiment, the processor 180 may transmit the received audio signal to a cloud server via the wireless communication unit, receive a result of natural language processing from the cloud server, and store the result along with the recorded video.

According to an embodiment, upon playing the video stored in the memory, the processor 180 may map the text, which corresponds to the speech recognition result stored together, to the mouth shape of the speaker included in the playing video, and display the same on the display (S1240).

FIG. 13 is a view illustrating the embodiment of FIG. 12.

Referring to FIG. 13, upon playing a recorded video, the speech recognition result may be displayed on the display to correspond to the movement of the speaker's mouth shape. As shown in FIG. 13, if a pause button is entered via the progress bar (PB) for controlling the playback of the stored video, the movement of the speaker's lips may be stopped, and the display of the speech recognition result may be stopped as well.

FIG. 14 is a view illustrating an example of setting a microphone's beamforming direction according to an embodiment of the disclosure.

Referring to FIG. 14, upon recognizing the speaker's direction, the processor 180 may display guide information for setting the beamforming direction of microphone on the display. The guide information may include graphic objects G1 and G2 corresponding to the number of the microphones. Upon receiving a gesture input (e.g., a drag input) to associate the specific graphic object G1 with a specific speaker SG1, the processor 180 may control to set the beamforming direction of the specific microphone G1 to the specific speaker SG1.

According to an embodiment, if the number of microphones is identical to the number of speakers, with the speaker's direction recognized via facial recognition and mouth shape recognition for the sound source direction, the processor 180 may not separately display the guide information. According to an embodiment, unless the number of microphones is identical to the number of speakers, guide information may be displayed to allow the user to set the microphone beamforming direction to correspond to the speaker.

The above-described disclosure can be implemented with computer-readable code in a computer-readable medium in which program has been recorded. The computer-readable medium may include all kinds of recording devices capable of storing data readable by a computer system. Examples of the computer-readable medium may include a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, a CD-ROM, magnetic tapes, floppy disks, optical data storage devices, and the like and also include such a carrier-wave type implementation (for example, transmission over the Internet). Therefore, the above embodiments are to be construed in all aspects as illustrative and not restrictive. The scope of the disclosure should be determined by the appended claims and their legal equivalents, not by the above description, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced therein.

LEGEND OF REFERENCE NUMBERS

-   -   100: electronic device 

What is claimed is:
 1. An electronic device, comprising: a memory; a display; a camera configured to capture a video; at least one microphone configured to obtain a sound while capturing the video; and a processor configured to: detect a direction of a sound source of the sound based on a sound feature of the obtained sound, recognize a face and mouth shape of a person positioned in the direction of the sound source, recognize that the person is issuing an utterance by lip reading analysis based on recognizing the face and mouth shape of the person, perform an audio zoom by performing beamforming of the at least one microphone based on a direction of the recognized person issuing the utterance, store, in the memory, the captured video together with text corresponding to a result of speech recognition on the utterance of the recognized person according to the performed audio zoom, and during playback of the captured video stored in the memory, map the text corresponding to the result of the speech recognition to the recognized mouth shape of the person issuing the utterance and associate the obtained sound with a representation of the recognized mouth shape of the recognized person issuing the utterance in the captured video by causing the display to display the text corresponding to the result of the speech recognition.
 2. The electronic device of claim 1, wherein the face of the person is recognized based at least in part on a first learning model trained for facial recognition from an image.
 3. The electronic device of claim 2, wherein the first learning model comprises an artificial neural network, and wherein results output from the artificial neural network comprise information for the recognized person issuing the utterance and positions of eyes, a nose, and a mouth of the recognized person issuing the utterance.
 4. The electronic device of claim 1, wherein the processor is further configured to recognize that the recognized person is issuing the utterance based at least in part on a variation in the recognized mouth shape of the recognized person issuing the utterance according to a second learning model.
 5. The electronic device of claim 1, wherein the processor is further configured to amplify a voice generated from the direction of the recognized person issuing the utterance and reduce sounds from other directions other than the direction of the recognized person issuing the utterance.
 6. The electronic device of claim 1, wherein the processor is further configured to control the beamforming of the at least one microphone based on a varied position upon recognizing a variation in a position of the recognized person issuing the utterance.
 7. The electronic device of claim 6, wherein the variation in the position of the recognized person issuing the utterance is recognized based on an image obtained via the camera.
 8. The electronic device of claim 1, wherein the processor is further configured to detect the direction of the sound source based on a determination that the sound obtained via the microphone includes a human voice based on an analysis of the sound.
 9. The electronic device of claim 1, wherein, the at least one microphone includes a plurality of microphones, and the processor is further configured to separate sounds obtained via the plurality of microphones into a same number of sound sources as a number of the plurality of microphones and to recognize the face and mouth shape of the person based on a direction of each sound source.
 10. The electronic device of claim 9, wherein the processor is further configured to group at least two or more of a plurality of persons issuing utterances according to a distance between the plurality of persons based on a determination that the number of the sound sources is larger than the number of the plurality of microphones and to control performing the audio zoom to perform beamforming in a direction of the grouped at least two or more of the plurality of persons.
 11. The electronic device of claim 9, wherein the processor is further configured to control at least two or more of the plurality of microphones to have a same beamforming direction according to a predetermined reference based on the number of the sound sources being smaller than the number of the plurality of microphones.
 12. The electronic device of claim 1, wherein the at least one microphone includes at least one of an omni-directional microphone that reacts to sounds from all directions at a same sensitivity, a uni-directional microphone that reacts to sounds from a front direction of the uni-directional microphone in a 180 degree range at a sensitivity not less than a threshold, or a super-directional microphone that reacts to sounds from a front direction of the super-directional microphone in a 30 degree range at a sensitivity not less than the threshold.
 13. The electronic device of claim 1, wherein the processor is further configured to adjust a gain of the microphone based on recognizing that the person issuing the utterance is issuing the utterance after the direction of the recognized person issuing the utterance is recognized and determining a direction of the beamforming.
 14. The electronic device of claim 1, wherein the processor is further configured to adjust a gain of the microphone depending on a distance from the recognized person issuing the utterance being captured via the camera.
 15. The electronic device of claim 1, wherein the text is displayed proximate to a display of the recognized mouth shape of the recognized person issuing the utterance.
 16. The electronic device of claim 1, wherein the processor is further configured to cause the display to display guide information for setting a direction of the beamforming based on recognizing a direction of the recognized person issuing the utterance.
 17. The electronic device of claim 16, wherein the guide information comprises graphic objects corresponding to a number of the at least one microphone, and wherein the processor is further configured to control setting a beamforming direction of a specific microphone to face the recognized person issuing the utterance according to reception of a gesture input to associate a specific graphic object with the recognized person issuing the utterance.
 18. The electronic device of claim 1, wherein the captured video is stored in a storage unit with synchronized voice data and image data of the video, and wherein the processor is further configured to analyze each of the image data and the voice data based on a determination that more than a predetermined number of frames are stored in the storage unit.
 19. A method of controlling an electronic device, the method comprising: capturing a video via a camera; obtaining a sound via at least one microphone while capturing the video; detecting a direction of a sound source of the sound based on a sound feature of the obtained sound; recognizing a face and mouth shape of a person positioned in the direction of the sound source; recognizing that the person is issuing an utterance by lip reading analysis based on recognizing the face and mouth shape of the person; performing an audio zoom by controlling a beamforming direction of the at least one microphone to face the recognized person issuing the utterance; store the captured video together with text corresponding to a result of speech recognition on the utterance of the recognized person according to the performed audio zoom, and during playback of the captured video stored, map the text corresponding to the result of the speech recognition to the recognized mouth shape of the person issuing the utterance and associate the obtained sound with a representation of the recognized mouth shape of the recognized person issuing the utterance in the captured video by displaying the text corresponding to the result of the speech recognition. 